Comparison of Design-Build and Design-Bid-Build Performance of Public University Projects

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Comparison of design-build and design-bid-build performance of public university projects

James David Fernane University of Nevada, Las Vegas

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This Thesis has been accepted for inclusion in UNLV Theses, Dissertations, Professional Papers, and Capstones by an authorized administrator of Digital Scholarship@UNLV. For more information, please contact [email protected]. COMPARISON OF DESIGN-BUILD AND DESIGN-BID-BUILD PERFORMANCE

OF PUBLIC UNIVERSITY PROJECTS

James David Fernane

Bachelor of Arts in Architecture

College of Environmental Design

University of California, Berkeley

A thesis submitted in partial fulfillment of

The requirements for the

Master of Science in Construction Management

Construction Management Program

Howard R Hughes College of Engineering

Graduate College

University of Nevada, Las Vegas

August 2011

THE GRADUATE COLLEGE

We recommend the dissertation prepared under our supervision by

James David Fernane

entitled

Comparison of Design-Build and Design-Bid-Build Performance of Public University Projects

be accepted in partial fulfillment of the requirements for the degree of

Master of Science in Construction Management Construction Management Program

Pramen Shrestha, Committee Chair

David Shields, Committee Member

Neil Opfer, Committee Member

Nancy Menzel, Graduate College Representative

Ronald Smith, Ph. D., Vice President for Research and Graduate Studies and Dean of the Graduate College

August 2011

ABSTRACT

Comparison of Design-Build and Design-Bid-Build Performance

of Public University Projects

James David Fernane

Dr. Pramen P. Shrestha, Examination Committee Chair

Assistant Professor

University of Nevada, Las Vegas

With an unsure market and scarce work, owners across the United States, especially universities, are finding themselves in situations where they are unable to complete their projects within cost and schedule using the traditional delivery method: Design–Bid–

Build (DBB). Under the DBB project delivery method, many competent contractors are electing to send low bids on projects just to keep work on their books, with plans to receive change orders once the project is underway; this practice is leading to cost and schedule overruns. Public universities across the United States are beginning to elect to use Design-Build (DB) as an alternate project delivery method over the traditional project delivery method of DBB in order to aid in reducing the cost, schedule, and change orders.

Due to current legislation in effect, all 50 states are able to use the DB delivery method. However, only 20 states and their public agencies are permitted to use DB for all types of design and construction projects. In 18 states, DB is widely permitted, but not all agencies are permitted to use this delivery method. In the remaining 12 states, DB is a limited option.

iii

In order to analyze and compare Design-Build (DB) and Design-Bid-Build (DBB)

projects, this study collected data, by means of convenient random sampling, from

construction projects built by Planning and Construction Departments of U.S.

universities. Statistical tests were conducted to determine if the metrics related to cost,

schedule, and change orders were significantly different from each other in these two

types of projects.

The findings of this study will help public universities decide what delivery

method is best for them in terms of controlling costs, schedule, and change orders. The

results showed that DB projects significantly outperformed DBB projects in terms of

Contract Award Cost Growth, Design and Construction Schedule Growth, Total Schedule

Growth, Construction Intensity, Construction Change Order Cost Growth, and Total

Change Order Cost Growth.

ACKNOWLEDGEMENTS

I would like to take this opportunity to thank those individuals who made this research project possible. First and foremost, I would like to thank Dr. Pramen P. Shrestha, my research advisor for his perpetual support, guidance, and patience. Without Dr. Shrestha’s support, this research thesis may not have been completed.

I would also like to thank all the university planning and construction directors and project managers across the United States of America for their time and willingness to complete the surveys sent to them.

I would also like to thank the University of Nevada, Las Vegas Construction

Management Department, particularly the faculty members Dr. David Shields and

Professor Neil Opfer, for their support and guidance through my graduate studies. I would like to thank Dr. Nancy N. Menzel, School of Nursing for providing me feedback.

I would also like to thank Mrs. Julie Longo for all her assistance with editing this paper.

I would also like to thank the University of Nevada, Las Vegas Planning and

Construction Department Director, David Frommer, and Assistant Directors, Timothy

Lockett and Robert Dincecco, for their support and willingness to incorporate my studies with my work schedule.

Lastly, I would like to share my sincere thanks for all the love and support offered to me from my wife, Jouse Fernane, my mother Sylvia Fernane, and my three brothers

Mike, Tom, and Roger Fernane.

This research paper is dedicated to my daughter Alexis Fiori Fernane with the hopes that one day I will be able to hold her in my arms again.

“Alexis, you are in my thoughts every day. I love you.”

TABLE OF CONTENTS

ABSTRACT ...... iii

ACKNOWLEDGEMENTS ...... v

LIST OF TABLES ...... viii

LIST OF FIGURES ...... ix

INTRODUCTION ...... 1

1.1 Design-Bid-Build Delivery Method ...... 2 1.2 Design-Build Delivery Method...... 6 1.3 Scope and Motivation of the Study ...... 9 1.4 Objectives of the Study ...... 10 1.5 Sequence and Significance of the Study ...... 11

LITERATURE REVIEW ...... 12

2.1Comparisons of DB and DBB Building Projects ...... 12 2.2 Highway Project Literature Review...... 22 2.3 Summary of Literature Review ...... 27

RESEARCH METHODOLOGY...... 28

3.1 Research Steps ...... 28 3.1.2 Review Literature...... 29

3.1.3 Develop Questionnaire...... 29

3.1.4 Collect Data ...... 30

3.1.5 Analyze Data ...... 31

3.16 Statistical Tests ...... 33

3.1.7 Make Recommendations and Conclusions ...... 36

3.2 Study Hypotheses...... 36

3.2.1 Research Hypotheses ...... 38

3.2.2 Null Hypothesis ...... 39

3.3 Limitations of the Study...... 41

DATA DESCRIPTION ...... 42

FINDINGS ...... 48

5.1 Descriptive Statistics ...... 48 5.2 One-way Analysis of Variance ...... 51 5.3 Normality Assumptions Test Results ...... 52 4.4 Results of Equal Variance Test ...... 63

CONCLUSION AND RECOMMENDATIONS ...... 71

6.1 Conclusions ...... 71 6.1.2 Schedule Growth ...... 72

6.1.3 Change Order Growth ...... 72

6.2 Recommendations for Further Study ...... 73

REFERENCES ...... 74

APPENDIX A ...... 76

APPENDIX B ...... 81

APPENDIX C ...... 86

APPENDIX D ...... 94

APPENDIX E ...... 102

APPENDIX F...... 108

VITA ...... 109

vii

LIST OF TABLES

Table 1. Advantages and Disadvantages of Design-Bid-Build (DBB) Method ...... 6 Table 2. Advantages and Disadvantages of the Design-Build (DB) Method ...... 9 Table 3. Literature Review Summary for Building Projects ...... 22 Table 4. Literature Review Summary for Highway Projects ...... 26 Table 5. Engineering News Record Building Cost Index ...... 33 Table 6.Engineering News Record Building City Index ...... 33 Table 7. Descriptive Statistics of Cost Metrics ...... 49 Table 8. Descriptive Statistics of Schedule Metrics ...... 50 Table 9. Descriptive Statistics of Change-Order Cost Metrics ...... 52 Table 10. Anderson Darling Test for Contract Award Cost Growth ...... 54 Table 11. Anderson Darling Test for Construction Cost Growth ...... 55 Table 12. Anderson Darling Test for Total Cost Growth ...... 56 Table 13. Anderson Darling Test for Cost Per Square Foot ...... 57 Table 14. Anderson Darling Test for Design and Construction Schedule Growth ...... 58 Table 15. Anderson Darling Test for Total Schedule Growth ...... 60 Table 16. Anderson Darling Test for Construction Intensity ...... 61 Table 17. Anderson Darling Test for Design Change-Order Cost Growth ...... 62 Table 18. Anderson Darling Test for Construction Change-Order Cost Growth ...... 63 Table 19. Anderson Darling Test for Total Change-Order Cost Growth...... 64 Table 20. Results of Homogeneity of the Variance test for Cost Metrics ...... 65 Table 21. Results of Homogeneity of the Variance test for Schedule Metrics ...... 65 Table 22. Results of Homogeneity of the Variance test for Change-Order Cost Metrics ...... 66 Table 23. ANOVA Results for Cost Metrics ...... 66 Table 24. T-test for Unequal Variance Results for Schedule Metrics ...... 68 Table 25. ANOVA and T-test for Unequal Variance Results for Change-Order Cost Metrics ...... 70

viii

LIST OF FIGURES

Figure 1.Contractual Relationship of the Design-Bid-Build (DBB) Method ...... 5 Figure 2.Contractual Relationship of Design-Build (DB) Method...... 8 Figure 3.Research Methodology Flow Chart ...... 28 Figure 4.Number of Design-Build (DB) Projects in Each State...... 43 Figure 5.Number of DB Projects Completed in Each Year ...... 43 Figure 6.Total Cost Range for DB Projects ...... 44 Figure 7.Total Design and Construction Duration in Months for DB Projects ...... 45 Figure 8.Number of DBB Projects in Each State ...... 45 Figure 9.Number of DBB Projects Completed in Each Year ...... 46 Figure 10.Total Cost Range for DBB Projects ...... 47 Figure 11.Total Design and Construction Duration in Months for DBB Projects ...... 47 Figure 12.Histograms of Contract Award Cost Growth ...... 54 Figure 13.Histograms of Construction Cost Growth ...... 55 Figure 14.Histograms of Total Cost Growth ...... 56 Figure 15. Histograms of Cost Per Square Foot ...... 57 Figure 16. Histograms of Design and Construction Schedule Growth ...... 58 Figure 17. Histograms of Total Schedule Growth ...... 59 Figure 18. Histograms of Construction Intensity ...... 60 Figure 19. Histograms of Design Change-Order Cost Growth...... 61 Figure 20. Histograms of Construction Change-Order Cost Growth ...... 62 Figure 21. Histograms of Total Change-Order Cost Growth ...... 63 Figure 22. Box Plots of Cost Performance Metrics ...... 67 Figure 23. Box Plots of Schedule Performance Metrics ...... 69 Figure 24. Box Plots of Change-Order Cost Performance Metrics ...... 71

CHAPTER 1

INTRODUCTION

In today’s ever-changing construction market, owners are finding themselves in many undesirable and unfamiliar situations. With an unsure market and scarce work, owners across the United States, especially universities, are finding themselves in situations where they are unable to complete their projects within cost and schedule using the traditional delivery method: Design–Bid–Build (DBB). Under the DBB project delivery method, many of the competent contractors are electing to send low bids on projects just to keep work on their books, with plans to receive change orders while it is underway, which is leading to cost and schedule overruns. Universities across the United States are beginning to elect to use Design-Build (DB) as an alternate project delivery method over the traditional project delivery method of DBB to aid in reducing the cost, schedule, and change orders.

Furthermore, this has led to unqualified contracting companies also bidding on jobs that utilize the traditional delivery method, DBB. This in turn is leading to even more change orders, cost overruns, and the inability to meet the schedule. With a selection process based on best value or qualifications, this problem can be avoided

(Scott et al. 2006).

Public agencies --for example, state funded universities that rely heavily on tight deadlines and compacted or accelerated schedules due to the service they provide for their student population -- are now searching for alternate delivery methods for projects.

One delivery method that increasingly is being considered is the DB delivery method.

Under the DB delivery method, the owner/client produces bridge documents for the basis 1

of the design and sets forth expectations for the design and construction of the project.

Then, the owner/client contracts with a single entity, which then becomes responsible for both the design and the construction of the project. Furthermore, the DB delivery method has criteria built into the selection process that allows the owner to select the DB entity based on the best value for the owner; in this way, the owner is not ‘handcuffed’ to the low bidder or to aforementioned unqualified contracting companies.

In order to aid in reducing cost and schedule overruns, universities across the U.S. are beginning to elect to use DB as an alternate delivery method over the traditional method of DBB. Due to current legislature in effect, all 50 states are able to use the DB delivery method. However, only 20 states and their public agencies are permitted to use DB for all types of design and construction projects. In 18 states, DB is widely permitted, but not all agencies are permitted to use this delivery method. In the remaining 12 states, DB is a limited option.

1.1 Design-Bid-Build Delivery Method

Under the Design-Bid-Build (DBB) delivery method, the owner selects a design firm to create contract documents consisting of project drawings (the design) and job specifications. Depending on the project size and complexity, the project drawings typically consist of seven main design disciplines: Civil, Architectural, Structural,

Mechanical, Electrical, Plumbing, and Telecommunications. After the design is completed, the project drawings become the contract documents and the project is awarded to the low bidder.

The job specifications can be listed on the drawings in note form; however, they are typically listed in special groups with section numbers designated by Construction 2

Specification Institute (CSI) Divisions 1 through 16. These divisions then are broken down into more categories within each of the 16 divisions, depending on the project size and complexity. Below outlines the typical layout of a 16-division CSI specification

Table of Contents. Recently in 2004, CSI introduced a new specification outline that includes 50 divisions; however, it is not widely used or popular at this time. Therefore, the projects completed in this study all used the 16-division format.

• Division 01 — General Requirements

• Division 02 — Site Construction

• Division 03 — Concrete

• Division 04 — Masonry

• Division 05 — Metals

• Division 06 — Wood and Plastics

• Division 07 — Thermal and Moisture Protection

• Division 08 — Doors and Windows

• Division 09 — Finishes

• Division 10 — Specialties

• Division 11 — Equipment

• Division 12 — Furnishings

• Division 13 — Special Construction

• Division 14 — Conveying Systems

• Division 15 — Mechanical

• Division 16 — Electrical

When the designer completes the contract documents (100% design completion), the job is advertised and/or delivered to selected companies to begin the bidding process.

General Contracting (GCs) companies acquire the contract documents and meticulously go through the plans and specifications to note all materials and work that need to be completed. Then the GCs prepare their final cost for all labor and materials, and submit this to the owner. This is considered their “Bid” for the job. Typically, the GCs’ bids must be submitted to the owner at a specific time and place; no late bids are accepted.

After the bids are accepted, opened, and reviewed by the owner, the GC with the lowest bid is offered the job, contingent on their ability to provide accurate insurance and bond coverage. If the GC is able to meet the insurance and bond requirements and accepts the job, a contract is signed and the work begins. Since the design is considered as the contract document, and was completed and issued by the owner, any changes that need to be done after the work begins are the owner’s responsibility. These changes are referred to as ‘change orders.’

Figure 1 shows the contractual relationship in the DBB delivery method. The straight arrowed lines indicate direct contractual relationships and the dashed line represents coordination aspects only.

Figure 1. Contractual Relationship of the Design-Bid-Build (DBB) Method.

To understand that no one project delivery method is flawless, Table 1 describes the

advantages and disadvantages of the DBB method. This may not include all the advantages and disadvantages known, but it does highlight the main points for a clearer understanding of this delivery method’s strengths and weaknesses.

Table 1. Advantages and Disadvantages of the Design-Bid-Build(DBB)Method.

Advantages of DBB Disadvantages of DBB

1. Owner controls design and 1. Requires significant owner expertise construction and resources 2. Design changes easily 2. Shared responsibility for project accommodated prior to start of delivery construction 3. Owner at risk to contractor for design 3. Design is complete prior to errors construction award 4. Design and construction are 4. Construction cost is fixed at sequential, typically resulting in contract award (until Change longer schedules Orders) 5. Construction costs unknown until 5. Low bid cost, maximum contract award competition 6. No contractor input in design, 6. Relative ease of implementation planning, or value engineering (VE). 7. Owner controls design/construction quality

1.2 Design-Build Delivery Method

Under the Design-Build (DB) delivery method, the owner produces bridging documents created by an Architect hired by the owner; these bridging documents provide the basis of the design that sets forth their expectations for the design and construction of the project.

Typically, these bridging documents contain schematic drawings and specifications in order that the DB entity understands how to create their DB proposal so that it can be tailored to the needs and desires of the owner.

When the owner’s Architect completes the bridging documents, the job is advertised and/or delivered to selected companies to begin the proposal process. This proposal process is somewhat different from the DBB bidding process since the DB entities have

the ability to alter the bridging documents and also have more freedom to tailor the

design to what that particular team believes is best for the owner and the project. These

changes to the bridging documents, of course, must be approved by the owner.

The DB entities acquire the bridging documents from the owner and meticulously go

through them in order to note all design, materials, and other work that needs to be

completed for their proposal. At that point, the DB entities prepare their final proposal

and submit them to the owner. This proposal is considered their “Bid” for the job, and

typically has a guaranteed maximum price (GMP). Also, the DB entities proposals

typically must to be turned into the owner at a specific time and place; no late proposals

are accepted.

After the proposals are accepted, the owner begins a lengthy review process that

includes different levels of criteria by which the proposals are judged and scored. This is

sometimes referred to as the ‘best value’ selection process. Criteria are built into the

selection process that allow the owner to select the DB entity based on the best value for

the owner; in this way, the owner does not have to be committed to a low bidder. The DB

entity that scores the highest in a sum of all the categories is offered the job, contingent

on their ability to provide accurate insurance and bond coverage. Unlike the DBB method, in which the lowest bidder is awarded the project, the DB entity that is chosen might not have the lowest price. If the DB entity is able to meet the insurance and bond requirements and accepts the job, a contract is signed and the work begins.

Since the DB entity creates the final design and specifications based off the bridging documents, the DB entity is responsible for the design and construction of the project; change orders will not be accepted unless they are owner-requested changes. Hence, the 7

owner contracts with a single entity that is responsible for the design and construction of the project.

Figure 2 shows the contractual relationship with the DB delivery method. The straight arrowed lines indicate direct contractual relationships and the dashed line represents coordination aspects only.

Figure 2. Contractual Relationship of Design-Build (DB) Method.

Table 2 lists the advantages and disadvantages Design-Build (DB) method. This may not include all the advantages and disadvantages known, but highlights the main points for a clearer understanding of this delivery method’s strengths and weaknesses.

Table 2. Advantages and Disadvantages of the Design-Build (DB) Method.

Advantages of DB Disadvantages of DB

1. Single entity responsible for design 1. Minimal owner control of both and construction design and construction quality 2. Construction often starts before 2. Requires a comprehensive and design completion, reducing project carefully prepared performance schedule specification 3. Construction cost is known and fixed 3. Design changes after construction during design; price certainty begins are costly 4. Transfer of design and construction 4. Potentially conflicting interests as risk from owner to the DB entity both designer and contractor 5. Emphasis on cost control 5. No party is responsible to represent 6. Requires less owner expertise and owner’s interests resources 6. Use may be restricted by regulation

1.3 Scope and Motivation of the Study

The scope of this research study will be to evaluate several different university projects using the traditional delivery method (DBB) and also the DB delivery method in order to determine which delivery method is the best approach to meet the needs of universities.

This research study was built on previous studies conducted on this topic involving building and highway construction; this study also used questionnaire surveys, by means of convenient random sampling, on projects recently completed by universities under

DB and DBB project delivery systems. Literature reviews on previous studies were analyzed, compared, and interpreted; the results then were applied to the current research problem.

The motivation behind this research study lies in the desire to find a solution to the delivery method problems being faced by universities and also to make universities aware of the different alternatives they have; in other words, they are not obligated to use the traditional delivery method, DBB. Furthermore, motivation is driven by the desire to help universities arrive at a more productive delivery method that meets their schedules and keeps their costs manageable.

Lastly, there are personal reasons. For the past 15 years, I have worked for various universities as a Project Manager. I have been in the industry for over 25 years, and have been faced with the problems and challenges presented by the traditional DBB delivery method. I plan to continue to work in a university environment for years to come, and hope that this research effort will aid in determining the correct delivery method to choose on a project-by-project basis.

1.4 Objectives of the Study

This research project focuses on metrics for cost, schedule, and change orders in both

DBB and DB projects built on 11 university campuses across the United States. The main objectives of this study are:

1. To determine whether the DB project delivery method is superior in terms of cost,

schedule, and change-order growth than DBB.

2. To develop a questionnaire for collecting data from DB and DBB university

projects for purposes of comparisons. 10

1.5 Sequence and Significance of the Study

The study began with a literature review of various different types of projects using DBB and DB project delivery methods. The study then moved forward to a literature review of public projects that used DBB and DB project delivery methods. During this literature review, presented in Chapter 2, no peer-reviewed papers could be found that were written about the use of the DB delivery method for university buildings. At that point, this research study on comparing DBB and DB project delivery methods for university buildings became a reality.

Chapter 3 of this paper discusses the methodology used to gather and analyze the project data in order to arrive at the conclusions drawn from this study. Chapter 4 describes the data gathered for this study. Chapter 5 presents the study’s findings and discusses which delivery method is superior in terms of cost, schedule, and change-order growth. Conclusions and some suggestions for further study related to the comparison of

DB and DBB methods are discussed in Chapter 6.

With the many current budget problems existing across the United States in public agencies, this study appears to be relevant in finding a solution that possibly could save states’ money on their public projects by reducing total cost, schedule, and change-order growth.

CHAPTER 2

LITERATURE REVIEW

Universities across the United States are now starting to move away from the traditional delivery method, DBB, and implement the use of alternate delivery methods, such as DB.

There have been many research studies done regarding DBB and DB delivery methods for public and private projects, highway and military projects, and general building projects. The majority of these studies has been of a qualitative nature, and has relied heavily upon surveys, empirical studies, and case studies. However, none of these papers referred specifically to university buildings. The review of the other papers proved to be extremely valuable in gaining knowledge and understanding different methods for project procurement as well as alternate delivery methods. This in turn contributed to the successful completion of this research project. This chapter will summarize the literature review of DB and DBB project delivery methods used for building projects and highway projects as they relate to university buildings.

2.1Comparisons of DB and DBB Building Projects

In order to conclude if one project delivery method is superior to the other, Hale et al.

(2009) compared the performance of DB and DBB projects at U.S. Naval Facilities

(NAVFAC) Navy Bachelor Enlisted Quarters built between 1995 and 2004. This study statistically compared time and cost growth of 39 DBB projects and 38 DB projects in terms of total project duration, fiscal year duration, project start duration, project duration per bed, time per bed, project time growth, cost growth, and cost per bed. The final

objective was to test the hypotheses for the aforementioned areas that the Design-Build methodoutperformed the Design-Bid-Build method.

The data for this study was collected from various different databases from NAVFAC and Eprojects; this data included project description, delivery method, original contract amount, final contract amount, original project start date, project completion date, and a category code. Any data not gathered from NAVFAC and Eprojects, such as project descriptions or cost estimate information, was completed by means of an interview process. Data for a total of 129 projects were collected, out of which 52 projects were eliminated; the data for the remaining 77 projects were analyzed. Statistical analysis was used to determine which project delivery method was better than the other, and ANOVA was used to determine if the differences were statistically significant.

Not all the projects were completed at the same time or location; therefore, adjustments for time and location also were considered. For time adjustments, the team used escalation tables based on inflation forecasts from the U.S. White House’s Office of

Management and Budget and the Historical Air Force Construction Cost Handbook . The area cost factor index, developed by the U.S. Department of Defense, was used for location adjustment.

Values for the mean, median, and standard deviation were evaluated in terms of total contract cost growth. The study’s findings showed that the mean, median, and standard deviation values of Cost Per Bed metrics and Cost Growth of DB projects were lower than that of DBB projects. Similarly, the schedule-related metric, Time Growth, was reported in terms of added days to a project’s end date instead of a percentage of the total project timeline. The results of this study showed that the mean, median, and standard 13

deviation values for Time Growth of DBB projects were higher than that of DB projects.

Similarly, the mean, median, and standard values of Project Duration, Fiscal Year

Duration, and Construction Start Duration were higher for DBB than DB projects. This also was true for the mean, median, and standard values of Duration Per Bed.

This study used ANOVA to determine whether the performance metrics of DB and

DBB samples in the study were statistically significant. This study’s results showed that the means of Cost/Bed for other costs and Cost/Bed for DB and DBB projects were statistically not different. Hale et al. concluded that the Cost Growth for DB projects

(2%) was significantly lower than the cost growth for DBB projects (4%) for that sample.

Furthermore, this study concluded that the project duration (667 days vs. 1398 days), fiscal year duration (864 days vs. 1064 days), and construction start duration (667 days vs. 771 days.) for DB projects were significantly lower than those for DBB projects. The study also revealed that DB projects were about one half that of DBB projects in project duration per bed (2.6 vs. 7.0), and time growth (76 vs. 194). In addition, DB projects outperformed DBB projects in construction start duration per bed (2.6 vs. 3.7) and fiscal years duration per bed (3.6 vs. 5.1). All these findings were statistically significant at alpha level 0.05. This study was related directly to the NAVFAC projects, and the samples were homogenous. The results showed that DB projects took less time, had less cost growth, and were less expensive to build in comparison to DBB projects.

A study by Konchar and Sanvido (1998) compared cost, schedule, and quality performance of 351 projects completed between 1990-1996 for Construction Manager at

Risk (CMAR), DB, and DBB projects. This research was divided into four different phases. Phase 1 developed the process of collecting and analyzing the data in terms of 14

cost, schedule, and quality. Phase 2 collected extensive project data from the U.S.

Construction Industry. Phase 3 checked the data for accuracy and completeness, and

Phase 4 tested univariate hypotheses to distinguish significant differences in delivery performance.

According to Konchar and Sanvido (1998), “Cost was defined as the design and construction cost of the base facility and did not include land acquisition, extensive site work, and process or owner costs. The three cost measures were unit cost, project cost growth, and intensity.” The time aspect was defined as “the total as planned time,” and was calculated from the planned start date to the planned construction end date.

A survey was used to collect specific data for each project. Seven thousand six hundred surveys were sent; only 378 surveys were completed, and of those, only 301 projects were useable for analysis. To standardize the data, the team adjusted each project cost by using historical cost indices for location and time. Several different statistical methods were used for analysis, such as univariate to compare means, medians, and standard deviations and multivariate linear regression to determine the effect of project delivery method on cost and schedule metrics.

Quality performance was measured in the following seven specific areas:1) start up;2) call backs;3) operation and maintenance;4) envelope, roof, structure, and foundation;5) interior space and layout;6) environment; and finally 7) process equipment and layout.

According to Konchar and Sanvido (1998), “Quality was recorded separately for the turn over process and for the performance of specific systems. This was done to eliminate any owner bias present from a highly difficult turn over process.”

The results showed that the performance of DB and CMAR projects were much better than for DBB projects in terms of startup quality, call backs, interior space and layout, and process equipment layout. For operation and maintenance, the study found that DB projects achieved superior performance over both CMAR and DBB projects in terms of quality; however, DB projects only showed significantly higher results than DBB projects for envelope, roof, structure, and foundation. In these specific areas, CMAR projects performed better than both DB and DBB projects.

Using multivariate regression analysis, the team developed three models to evaluate the changes in unit cost, construction speed, and delivery speed. The study showed that

DB projects outperformed DBB and CMAR projects by less than 6.1 percent and 4.5 percent, respectively, regarding unit cost. The authors also identified four variables that have the greatest impact on unit cost: Contract Unit Cost, Facility Type, Project Size, and

Project Delivery System. The regression analysis showed that these five variables accounted for about 99% of the variations in unit cost.

In addition, the study showed that the construction speed of DB projects was faster than for both DBB and CMAR projects by 12 percent and 7 percent, respectively. The findings were significant at alpha level 0.05. There were six variables that have accounted for 89% of the variation in construction speed: 1) project size, 2) contract unit cost, 3) project delivery system, 4) percent design complete before the construction entity joined the project team, 5) project team communication, and 6) project complexity.

The last finding of this study was related to overall project delivery speed. In terms of overall delivery speed, the study showed that DB projects were approximately 33.5 percent faster than DBB projects and 23.5 percent faster than CMR projects. The 16

significant variables that have an impact on this delivery speed were project size, contract unit cost, percent design complete before construction entity joined the project team, facility type, and project team communication. The authors found two variables that had lesser impact on delivery speed performance:1) excellent subcontractor experience with the facility and 2) project complexity.

Overall, Konchar and Sanvido (1998)evaluated the performance of DB, CMAR, and

DBB projects from data collected from 351 projects built in the U.S. from 1990-

1996.From this sample of projects, they showed that that DB projects are superior and outperformed CMAR and DBB projects in terms of cost and schedule.

Ling et al. (2004) predicted project performance in terms of cost, schedule, quality, and owner’s satisfaction for both DB and DBB projects, using data collected from 87 building projects for 11 variables. According to Ling et al. (2004), “The objectives were to find variables that affect project performance and to construct models to predict DB and DBB project performance. With the outcomes and models produced, owners may be able to choose which delivery method is best for their project.”

The research methodology used wasa case study questionnaire based on past projects sent to owners, contractors, and consultants. Forty owners were asked to complete 49 project surveys, 60 contractors were asked to complete 180 project surveys, and 57 consultants were asked to complete surveys for 171 projects. A total of 87 project surveys were completed for 54 DBB projects and 33 DB projects. The data gathered from these projects were inserted into SPSS statistics software, and 24 possible models were produced to predict cost and construction intensity. This study showed that different variables, and sometimes shared variables, affected each metrics performance; a 17

comparison of the 11 models that predict project performance in DB and DBB projects is described below.

The comparison of the cost models of DB and DBB projects showed that only the

Unit Cost model did not share any similarities; on the other hand, both Cost Growth and

Intensity models shared similar variables, such as the contractors’ paid-up capital and design completion when the budget is fixed, that affected project performance. The time- related models for DB and DBB projects showed that both construction speed and delivery speed were affected by the gross floor area of the building, while Schedule

Growth models did not share any similarities. The comparison of the quality models showed no similarities that affected project performance in DB and DBB projects. The

DB and DBB models that compared owner satisfaction showed that the only similar variable that affected project performance was the contractor’s technical expertise.

Furthermore, the results showed that buildings designed and constructed by public entities tended to be more expensive than buildings designed and constructed under private ownership. In DB projects, the cost fluctuated up to 42% more expensive, depending on the extent of the design completion in the bid documents. Typically, the cost will increase when the owner initiates more of the design. The more prescriptive the design, the higher the cost may be. This study further suggested that cost growth for DB and DBB projects would be higher if contractors with less capital were contracted.

In addition, Ling et al. (2004) produced models for forecasting Construction Intensity, in which the larger the project, the greater the construction intensity. This is attributed to the use of more sophisticated equipment and the possibility for prefabrication of certain building elements. This study agreed with one conducted by Molenaar and Songer 18

(1998),who stated, “The degree of urgency of the project affects schedule growth.”This means that if more pressure were put on DB projects to accelerate the schedule and if

DBB projects had the proper amount of manpower, the construction intensity would be improved. Quality also was analyzed during this study; the authors found that reviewing the contractors’ resumes of past projects as well as the outcomes of those projects is a main predictor of the current and future quality of work to be expected from a particular contractor.

The owner’s satisfaction is directly related to the contractor’s track record, expertise, safety, and quality. Ling et al. (2004) found that 68% of owner’s satisfaction for DB projects is related to the contractor’s specialized project experience and safety record.

DBB project owners based their satisfaction on previous track record, number of change orders submitted during each project, and flexibility of scope. A good analogy for a DB project building for a university laboratory would be if one contractor completed five laboratory projects with no injuries in the previous three years and another contractor complete done laboratory project with two injuries in the previous five years; comparing these two records, an owner would look favorably upon the first contractor.

Ibbs et al. (2003) compared DB and DBB projects to determine which delivery method was more effective. This study evaluated the influence that a project delivery method, such as DB and DBB, may have on the outcome of the project. Information on cost, schedule, and productivity were collected from the Construction Industry Institute

(CII).This study developed a questionnaire that included questions involving project delivery methods as well as changes in cost and schedule, which were was used to request data on project information. The CII sent surveys to over 100 projects located in 19

the U.S., Canada, Middle East, and Latin America that included questions regarding basic project information, cost, schedule, and productivity information. Surveys from 67 projects were collected that included “name, location, contract type, owner information, cost, schedule, and productivity performance.” The original budget of each project was subtracted from the final cost to determine the cost change, and the schedule change was calculated by subtracting the estimated duration from the final duration. The productivity was calculated as earned labor-hours divided by expected labor-hours.

This study showed that DB projects had less cost changes (13%) than DBB projects

(15.6%). According to this research study, DBB projects had decreased changes (-0.4%) while DB projects had about 7.4% increased changes. This result indicates that when a project used the DB method, the cost increased.

Further research in this study showed that during the construction phase, projects that used the DB method had approximately 4% increase in cost changes, while DBB had about 9% decrease in cost changes. In the design phase, DB projects had an average cost change of 8% and DBB had an average change in cost of 9%. The changes in schedule showed that DB projects outperformed DBB projects by having only a 7.7% change, while DBB projects had an 8.4% change in schedule. This study also compared productivity against schedule and cost changes in regards to the delivery method used by the project. The study showed that when each delivery method had the same amount of schedule change, then DBB projects outperformed DB projects in terms of productivity.

In conclusion, this study by Ibbs et al. (2003) showed that DB projects had a higher total cost change than DBB projects, but DB projects outperformed DBB projects in

terms of schedule. Additionally, when productivity was compared, both DB and DBB projects had approximately the same amount of change with respect to the project.

Wardani et al. (2006) stated that, “Several studies have analyzed the growing trend towards the use of Design-Build delivery method and the shift from more traditional delivery methods.” This research on the procurement method of project delivery systems strays a bit from the topic of this thesis; however, procurement methodologies of delivery methods are almost as important as the delivery method itself. The data analysis indicated several important trends associated with different performance metrics. Results from this study showed that the low-bid selection process had the highest cost growth, which was

9% higher than the qualifications-based procurement method. This study showed that schedule growth from the best value procurement method had an average of 0% schedule growth. Therefore, even though the DB delivery method can possibly lead to superior project performance, the procurement methodology used to select the DB firm should be evaluated very carefully prior to advertising.

Table 3. Literature Review Summary for Building Projects. Researchers Methods Sample Project Types Major Findings Size Hale et al. DB 38 Navy Bachelors Living DB cost and (2009) DBB 39 Quarters schedule metrics were significantly better than DBB Konchar and DB 155 Industrial Buildings DBB unit cost Sanvido DBB 116 growth is 6.1% (1998) CMAR 80 higher than DB and DB construction speed was 12% higher than DBB Ling et al. DB 33 Building projects DB and DBB (2004) DBB 54 construction and delivery speed can be predicted with six variables Ibbs et al. DB 24 Building projects DBB schedule (2003) DBB 30 growth was 2.4 % higher than DB and DBB cost growth was 7.8% lower than DB Wardani et al. DB 76 Procurement method and LBDB had a 9% (2006) performance higher cost growth than that of BVDB and BVDB had a 0% schedule growth

2.2 Highway Project Literature Review

Gransberg and Senadheera (1999) studied three different methods that State Departments of Transportation are implementing in their DB procurement: low bid DB (LBDB), adjusted score DB (ASDB), and best value DB (BVDB).During the LBDB process, proposals and prices are submitted. The owner agency opens the bids and compares the prices to find the low bidder. Then, the designs are evaluated to ensure technical compliance with the RFP after disclosing the price. The author found that the low-bid 22

approach typically was used when the project was well defined and almost prescriptive.

The adjusted score DB approach was used when the project scope was not as well defined and alternatives in the design and materials were being considered. The best value DB approach was used when the owner was seeking creative design alternatives and where the owner would like to consider the technical experience of the contractor in the selection process.

All three of these delivery methods have their positive and negative aspects within the delivery process. LBDB is the easiest to implement and the most politically accepted method of the three because it involves accepting the lowest price. The weakness of the

LBDB approach is that it does not allow the DB firms to implement different design solutions for the same project. ASDB allows a rating scale for designers and builders while reaping the benefits of innovative approaches to the project. The disadvantage of this approach is that it may weed out options that are initially more expensive for options that have a shorter life cycle. Finally, BVDB is very amendable and open-ended, allowing for the project needs to be met very closely. Price is only one of several different factors considered during the evaluation process, so this approach encourages innovation. The major drawback of BVDB is the development of the RFP and the complexity of the evaluation planning.

Since all highway projects are unique in their own way, the choice of what procurement method to use needs to be evaluated on a project-by-project basis. In this way, the correct procurement method can be chosen that maximizes the possibility of selecting the best contractor for the project.

Warne (2005) studied 21 highway projects to determine the effectiveness of the DB project delivery method. Questionnaires were sent out to project managers across the country for 21 DB projects, comparing DB performance with the DB process. The questionnaires had several hypothetical questions regarding project information, cost, and the reason for using the DB method; project selection methodology; owner assessment; and quality. After the questionnaires were received, the author reviewed the data for schedule, cost, quality, and owner satisfaction. The results from the analyzing schedule data showed that 13 out of 21 projects chose DB as a project delivery method due to schedule effectiveness. The study showed that 26 percent of the DB projects were completed ahead of schedule, typically one to two months ahead of schedule. When the interviewees were asked how the project schedule would have been affected if the delivery method was DBB, 100% stated that the project would have taken longer than it did with the DB method.

Cost performance also was studied to compare the bid amount with the total completion cost. The author defined cost growth as the difference between the bid amount and the final cost of the project. In this case study, the result for cost growth in

DB projects was less than four percent compared to DBB projects, indicating that DB projects have less cost growth than DBB projects.

In addition, owner satisfaction in regards to quality of the work performed while using the DB delivery method was addressed in this study. In all 21 cases, it was determined that DB projects have equal to or better quality than if the project was delivered under the DBB method.

Shrestha et al. (2011) compared the relationship of DBB and DB projects for large

highway projects in terms of cost, schedule, and change order per lane mile. According to

Shrestha et al. (2011), the criteria used to select the DBB projects were as follows:

“1) The projects should only involve construction of roadways, 2) the construction

completion time of the project should be after 2000 and should not go beyond

2009, 3) the design and construction cost of the projects should exceed

$50,000,000.00, and 4) the projects should be constructed in the state of Texas. The

criteria for the DB projects were: 1) the projects should only involve construction

of roadways, 2) the highway projects are to be selected from FHWA SEP-14

projects, 3) the construction completion time of the project should be after 2000

and should not go beyond 2009, and 4) the design and construction cost of the

projects should exceed $50,000,000.”

The data was gathered in forms of questionnaires, and subsequent phone interviews, and internet searches. After the data was verified, it was analyzed using ANOVA and a t-test assuming unequal variances. The analysis showed that one lane mile of DB projects was designed in one half of a month and one lane mile in DBB projects were designed in two months. The construction speed per lane mile for DB projects was 11 days, and the construction speed per lane mile for DBB projects was 29.4 days. The cost per change order for DB projects was about 50 percent more than the cost per change order for DBB projects. However, the analysis did show that the number of change orders were lower in

DB projects (25 change orders) than DBB projects (65 change orders).

The study also researched project characteristics (input variables) and project performance (output variables) from large highway projects. This study showed that 25

14input variables had an alliance with one or more of the output variables. The input variables related to cost growth had a significant alliance with the amount of days lost with the increase of cost. The input variables related to cost per mile had significant alliance with the following four output variables. When a bridge area was compared, the cost per lane mile increased as design work hours per week decreased. The cost also increased as right of ways (ROWs) increased; this includes ROWs by eminent domain.

When evaluating schedule growth, the main finding here was that the use of partnering or bonuses resulted in lower schedule growth. Delivery speed could be increased if the project had fewer interchanges, fewer bridges, partnering, and less environmental evaluations. The cost per change order was also evaluated, and showed that new construction had fewer change orders than a reconstruction project.

Furthermore, the cost of change orders increased as the work days per week increased.

Table 4. Literature Review Summary for Highway Projects. Researchers Methods Sample Project Types Major Findings Size Gransberg DB N/A DB procurement LBDB,ASDB, and and DBB N/A methods BVDB are all valid Senadaheera procurement methods (1999) for DB Warne (2005) DB 21 Highway projects DB projects are typically completed one to two months ahead of schedule. Also DB has less cost growth than DBB Shrestha et al. DB 22 Highway projects Construction speed and (2010) DBB project delivery speed per lane mile of DB projects are significantly faster than that of DBB projects per lane mile

2.3 Summary of Literature Review

The literature review conducted during this research project can be summarized as follows. It appears that DB may be a more effective delivery method over DBB in regards to cost, schedule, and change order growth. However, one study by Ibbs et al.

(2003) found that the DBB method was more effective than DB.

To date, there have been no studies done comparing DBB and DB delivery methods on public university buildings in terms of cost, schedule, and change order growth. The findings of this current study will help the public universities decide what delivery method is best for them in terms of controlling cost, schedule, and change orders.

CHAPTER 3

RESEARCH METHODOLOGY

3.1 Research Steps

The steps involved in the research methodology are depicted in Figure 4 and are

described in this section. The research used statistical analysis to compare performance

metrics for cost, schedule, and change-order cost for DB and DBB projects at U.S.

universities.

Figure 3. Research Methodology Flow Chart.

3.1.1 Develop Objectives and Scope

The first step of the research project was to formulate a problem statement that describes the objectives, and the research scope. The details, including research background, the purpose of this study, objectives, and scope were addressed in Chapter 1.

3.1.2 Review Literature

A literature review was conducted on DB and DBB project delivery methods on building projects, and highway projects as they relate to university buildings. The literature review was discussed in Chapter 2.

3.1.3 Develop Questionnaire

Separate questionnaires were developed for DB and DBB projects in order to take into account the two different delivery methods and to ensure that the two types of projects were compared as precisely as possible. The literature review provided examples of other questionnaires used in previous studies; this proved helpful in the creation of the questionnaires for this study.

Each questionnaire for this study had a section for general project information, including location and contact information; and a section for project characteristics, such as square feet, construction type, and construction year. There was a section in both the

DB and DBB questionnaires for project performance, which included performance metrics for cost, schedule, and change orders. The cost and schedule information was collected differently for these two types of projects. For DB projects, data for cost, schedule, and change orders were combined with data for design and construction; for

DBB projects, information was collected separately for design and construction.

3.1.4 Collect Data

When the research began, the intention was to only concentrate on university buildings in the State of Nevada. Since the laws and regulations in Nevada (NRS 408.388) have been in effect only since 1999, and then expanded in 2001, a limited number of projects were delivered under a DB contract. Therefore, the study was broadened to include universities from Southern California. Once again, due to the limitation of completed DB projects, there still was not enough data. At that point, the study was expanded to as many universities as possible across the United States. Even so, during the data collection phase, it was found that many universities chose to use only DBB or Construction

Manager at Risk delivery methods, despite legislation that allowed them to utilize DB contracts.

Beginning in April 2010, a total of 300 questionnaires were sent to 230 universities, individual state universities as well as public and private university systems. From May

2010 to January 2011, 119 questionnaires were collected from universities in 11 states.

Since the study is concentrating on new building projects, 22 completed questionnaires had to be discarded from the study because the projects included remodeling of existing buildings, athletic fields, and parking structures. Furthermore, 16 questionnaires were returned incomplete; after consulting with the participants, the information was no longer available for 13 of these questionnaires, so they were discarded from the study as well. A total of 84 questionnaires, for 42 Design-Build projects and 42 Design-Bid-Build projects, were used for this study.

During the data collection phase many obstacles and barriers were encountered with the questionnaire response rate. Many of the project managers had difficulty finding the 30

time to complete the questionnaires, locating the data from archives, trying to locate project information that was no longer available (many project files were lost or discarded), and sometimes funding was an issue in filling out the questionnaires. It was mentioned that with the state budget cuts and staff being laid off there wasn’t enough time for the project managers to fill out the questionnaires and it would not be wise to spend the states money to have administrative assistants locate the project data and fill out the questionnaires. However, many project managers did have their administrative assistants fill out the questionnaires on their behalf.

3.1.5 Analyze Data

The type of projects collected for data analysis were university projects that were contracted and constructed under DBB and DB delivery methods. A detailed questionnaire was developed and sent to universities across the United States, requesting specific project information for both DBB and DB projects, as described in Section 3.1.4.

After all the questionnaires were reviewed for completeness, and the incomplete questionnaires completed by talking to the participants, the data for all 84 projects were entered into an Excel spreadsheet for processing. To properly sort and create formulas within the Excel spreadsheet, DB projects were labeled “1” and DBB projects were labeled “2.”

To precisely perform the statistical tests on cost in relation to time and location, adjustments were made to the data, using the building cost index and the local index.

Table 5 displays Engineering News Records (ENR) building cost indices. The costs of all the projects were converted to an equivalent cost of a 2011 project located in Los

Angeles, California. ENR records only 20 major cities in the location index; therefore, 31

the location index of projects that were not from those cities was taken from the cities nearest to them. For example, for projects from Las Vegas Nevada, the projects were considered to have been built in Denver, because Las Vegas’ city cost can be assumed to be equal as Denver rather than to Los Angeles.

Table 5. Engineering News Record Building Cost Index. Year Building Cost Index Year Building Cost Index 2001 3574 2007 4485 2002 3623 2008 4691 2003 3693 2009 4769 2005 3984 2010 4883 2006 4205 2011 4988

The cost index factor was calculated in order to change the cost of any year to be equivalent to the cost in 2011. Equation 1 was used to convert the cost of each project to a 2011 equivalent cost.

2011

2011 … . 1

After the cost was converted to an equivalent cost of 2011, then the location index was used to bring all the project cost equivalent to a project built in Los Angeles. Table 6 displays the ENR building city index.

Table 6. Engineering News Record Building City Index. Name of Cities Location Index Name of Cities Location Index Detroit 5198 Denver 4123 Los Angeles 5354 Atlanta 3789 Dallas 3808

Equation 2 was used to convert the project costs to represent a project built in Los

Angeles.

2011

. . 2

The hypothesis for this study is that for university buildings in the United States, the mean cost, schedule, and change order growth are significantly different in Design-Build projects than in Design-Bid-Build projects.

3.16 Statistical Tests

The data was analyzed using Analysis of Variance (ANOVA) test, Levene’s test, the

Anderson Darling test, and a t-test with unequal variances.

To use the ANOVA test, the following four assumptions must be met: 1) the sample should be randomly selected or by means of a convenient random sampling,, 2) the dependent variables should be in an interval scale or a ratio scale, 3) the dependent variable should be normally distributed, and 4) the variances of the two groups should be equal.

Levene’s test is used to assess variance homogeneity, which is a precondition for such parametric tests as the t-test and the ANOVA test. If the significance from Levene’s test is less than 0.05, then variances are significantly different and parametric tests cannot be used. Levene’s test hypothesized that the variances of two groups are the same.

The Anderson Darling test is used to test for normality. This test rejects the hypothesis of normality when the p value is less than or equal to 0.05. Rejecting the

normality test allows the researcher to state with 95% degree of confidence that the data 33

does not fit the normal distribution. Failing to reject the normality test only allows the

researcher to state that the data is normally distributed.

The t-test with unequal variances is used to check whether the means of two sets of

samples are significantly different in the case where their variances are not equal. The

typical way of doing this is by stating that in the null hypothesis, the means of the two

sets of samples are equal. The t-test used in this study assumes a normal distribution and

unequal variances.

The statistical programs that were used for this study were 1) Predictive Analytics

Software (PASW), now known as the Statistical Package for Social Sciences (SPSS) and

2) Microsoft Excel. In order to draw conclusions for this study, the ANOVA and

descriptive statistical tests were performed using SPSS; the t-test with unequal variances

was performed using the Excel data analysis package.

The ANOVA test compared the means of cost, schedule, and change-order

performance metrics of university buildings designed and constructed under both DB and

DBB project delivery methods, whose variances were equal. This study consists of 10

research hypotheses and 10 null hypotheses, represented by and , respectively. The null hypotheses are the direct opposites of the research hypotheses. Each null hypothesis will be rejected if the p value is less than 0.05 (Levine et al 2007). The 10 research hypotheses and 10 null hypotheses have been presented in this chapter in Sections 3.2.1 and 3.2.2.

To begin the ANOVA analysis, the data was checked for variation within and among groups. The variation between the two sample sets was determined by the sum of the

squared differences between each observation and the overall mean of the sets. The mean

squares were calculated by using the Equations 3, 4, and 5:

… … … … . . . 3 1 1

where ( c - 1) represents the degrees of freedom and c is the number of groups.

… . . . 4

where n is the sum of the sample sizes from all groups.

………………………..5 1

If there are no differences seen in the means and the null hypothesis is accepted, then all

three mean squares provide the overall variation in the data. To maintain accuracy, the F-

test is implemented, which is the ratio of MSA and MSW. The mathematical formula for

the F-test is stated in Equation 6.

…………………….. 6

A null hypothesis can be rejected if a determined alpha level of significance falls above the critical value because the F-test follows an F distribution with ( c - 1) degrees of freedom.

Otherwise, do not reject .

Results and further discussion in regards to the statistical tests performed in this

research study are explained in more detail in Chapter 4.

3.1.7 Make Recommendations and Conclusions

The conclusions drawn from the study findings are discussed in Chapter 6. Similarly, the

recommendations are also made in Chapter 6.

3.2 Study Hypotheses

The study hypotheses in relation to cost, schedule, and change-order cost were

formulated to determine whether one delivery method is superior to another delivery

method. Before developing research hypotheses, the performance metrics used to

compare these two delivery methods were developed. To compare these two delivery

methods, four metrics that are cost-related, three that are schedule-related, and three

metrics related to change-order costs were developed. Equations 7-16 show the formulas

used to calculate these metrics.

100. . 7

100. . 8

100. . 9

…………………10

(%)

100. . 11

100. . 12

(SF/Day)

22 ………………………..13

100………………………………………14

100……………………………….15

100……………….16

3.2.1 Research Hypotheses

There are 10 research hypotheses formulated fort this study. They are:

1. The mean Contract Award Cost Growth is significantly lower in DB projects than in

DBB projects for U.S. university buildings.

2. The mean Construction Cost Growth is significantly lower in DB projects than in

DBB projects for U.S. university buildings.

3. The mean Total Cost Growth is significantly lower in DB projects than in DBB

projects for U.S. university buildings.

4. The mean Total Cost Per Square Foot is significantly lower in DB projects than in

DBB projects for U.S. university buildings.

5. The mean Design and Construction Schedule Growth is significantly lower in DB

projects than in DBB projects for U.S. university buildings.

6. The mean Total Schedule Growth is significantly lower in DB projects than in DBB

projects for U.S. university buildings.

7. The mean Construction Intensity is significantly higher in DB projects than in DBB

projects for U.S. university buildings.

8. The mean Design Change-Order Cost Growth is significantly lower in DB projects

than in DBB projects for U.S. university buildings.

9. The mean Construction Change-Order Cost Growth is significantly lower in DB

projects than in DBB projects for U.S. university buildings.

10. The mean Total Change-Order Cost Growth is significantly lower in DB projects than

in DBB projects for U.S. university buildings.

3.2.2 Null Hypothesis

To conduct the statistical test, the above research hypotheses are converted to null

hypotheses. The null hypothesis always assumes that the means of two groups are equal.

The null hypotheses are described below.

1. The mean Contract Award Cost Growth in DB projects is equal to the mean Contract

Award Cost Growth in DBB projects for U.S. university buildings. The null

hypothesis is mathematically written as in Equation 17.

2. The mean Construction Cost Growth in DB projects is equal to the mean

Construction Cost Growth in DBB projects for U.S. university buildings. The null

hypothesis is mathematically written as in Equation 18.

3. The mean Total Cost Growth in DB projects is equal to the mean Total Cost Growth

in DBB projects for U.S. university buildings. The null hypothesis is mathematically

written as in Equation 19.

……………………………………..

4. The mean Total Cost per Square Foot in DB projects is equal to the mean Total Cost

Per Square Foot in DBB projects for U.S. university buildings. The null hypothesis is

mathematically written as in Equation 20.

………...

5. The mean Design and Construction Schedule Growth in DB projects is equal to the

mean Design and Construction Schedule Growth in DBB projects for U.S. university

buildings. The null hypothesis is mathematically written as in Equation 21.

…………………

6. The mean Total Schedule Growth in DB projects is equal to the mean Total Schedule

Growth in DBB projects for U.S. university buildings. The null hypothesis is

mathematically written as in Equation 22.

………………………..

7. The mean Construction Intensity in DB projects is equal to the mean Total Schedule

Growth in DBB projects for U.S. university buildings. The null hypothesis is

mathematically written as in Equation 23.

……………….

8. The mean Design Change-Order Cost Growth in DB projects is equal to the mean

Design Change-Order Cost Growth in DBB projects for U.S. university buildings.

The null hypothesis is mathematically written as in Equation 24.

…………………………………….

9. The mean Construction Change-Order Cost Growth in DB projects is equal to the

mean Construction Change-Order Cost Growth in DBB projects for university

buildings. The null hypothesis is mathematically written as in Equation 25.

…………..

10. The mean Total Change-Order Cost Growth in DB projects is equal to the mean Total

Change-Order Cost Growth in DBB projects for U.S. university buildings. The null

hypothesis is mathematically written as in Equation 26.

…………………………..

3.3 Limitations of the Study

This research study was conducted using data from public universities across the United

States and did not include private universities. This was because project information for public universities is considered “public information,” unlike private universities.

Therefore, it was easier for the project managers of public university to obtain this information and to get the questionnaires returned. In addition, when private universities failed to return questionnaires and an inquiry was made, the project managers stated that they were directed not to fill out the questionnaires. Therefore, the findings of this study are applicable only to the public university projects of U.S. Care should be taken to interpret the results of this study for other types of projects.

CHAPTER 4

DATA DESCRIPTION

The type of projects collected for data analysis were university projects that were contracted, designed, and constructed under both the DB and DBB delivery methods. A detailed questionnaire was developed and sent to University Planning and Construction departments across the United States. The questionnaires requested specific project information for both DBB and DB projects.

The histogram in Figure 4 shows the number of DB projects with respect to location.

This histogram indicates shows that the maximum number of projects was collected from universities in California and Arizona. California and Arizona began using the DB delivery method in public projects in 1999 and 2000, respectively, and determined this method worked well in their procurement system. Since then, both California and

Arizona began to implement the DB project delivery method on a more regular basis; as a result, these states have more projects completed under the DB delivery method than other states. This histogram is a result of this study, and is showing that California and

Arizona returned more completed questionnaires on DB than the other states listed.

20 18 16 14 12 10 8 6 4 2 0

Figure 4. Number of Design-Build (DB) Projects in Each State.

The histogram in Figure 5 shows the total number of projects started or completed within a specific year. This histogram indicates a growing trend of implementing DB projects for university buildings; this trend began in 2002 and was at its highest level in

2007.

10 9 8 7 6 5 4 3 2 1 0

Figure 5. Number of DB Projects Completed in Each Year.

The histogram in Figure 6 shows the total cost range for the DB projects collected in this study. Approximately 31% of the DB projects collected in this study had a cost range 43

of $10 million to $20 million. About 33% of the DB projects collected was a combination of projects ranging from $1 million to $10 million and projects ranging from $20 million to $30 million. The remaining 36% of the DB projects ranged from $0 to 1 million and from $40 million to above $90 million.

14 12 10 8 6 4 2 0

Figure 6. Total Cost Range for DB Projects.

The histogram in Figure 7 shows the total number of DB projects with respect to the total duration of design and construction, in months. For this study, only one DB project was collected for the range of 0-6 months and one DB project for the range of 54-60 months; the other 40 DB projects collected in this study ranged from 6 months to 42 months total duration.

10 9 8 7 6 5 4 3 2 1 0 <6 6-12 12-18 18-24 24-30 30-36 36-42 42-48 48-54 54-60 >60 months months months months months months months months months months months

Figure 7. Total Design and Construction Duration in Months for DB Projects.

The histogram in Figure 8 shows the number of DBB projects with respect to location. The study received the highest response rate from Wisconsin on DBB questionnaires, followed closely by California, Nevada, and Arizona. Again, this histogram does not suggest that Wisconsin completed more DBB projects than the other states listed; however, Wisconsin returned more questionnaires on DBB projects than any of the other states listed.

14 12 10 8 6 4 2 0

Figure 8. Number of Design-Bid-Build (DBB) Projects in Each State

The histogram in Figure shows the total number of DBB projects started or completed within a specific year. This figure shows that this study collected the highest amount of

DBB questionnaires for projects beginning or ending in 2006, followed by 2007, 2008, and 2004.

8 7 6 5 4 3 2 1 0

Figure 9.Number of DBB Projects Completed in Each Year.

The histogram in Figure 10 shows the total cost range for the DBB projects collected in this project. Approximatley 44% of the DBB projects collected in this study had a cost range of $1 million to $10 million. The remaining 56% of the DBB projects ranged from

$0 to $1 million and from $10 million to above $90 million.

20 18 16 14 12 10 8 6 4 2 0

Figure 10. Total Cost Range for DBB Projects.

The histogram in Figure 11 shows the total number of DBB projects with respect to total duration of design and construction, in months. Over 85% of the DBB projects collected for this study had a total design and constrcution duration ranging from 12 months to 54 months. The remaining 15% of the DBB projects ranged from 0 to 12 months and 54 to over 60 months.

8 7 6 5 4 3 2 1 0 <6 6-12 12-18 18-24 24-30 30-36 36-42 42-48 48-54 54-60 >60 months months months months months months months months months months months

Figure 11. Total Design and Construction Duration in Months for DBB Projects.

CHAPTER 5

FINDINGS

The performance data of DB and DBB projects were analyzed. First, the descriptive statistics of performance metrics related to cost, schedule, and change orders were calculated. Then, a one-factor Analysis of Variances (ANOVA) test and a t-test with unequal variance were conducted to determine whether the performance metrics of DB and DBB projects were statistically different from each other.

5.1 Descriptive Statistics

Table 7 shows the mean, median, and standard deviation of the cost performance metrics.

The results indicate that the mean Contract Award Cost Growth of DB projects (-11.1%)

is lower than that of DBB projects (-2.8%). The median values for both DB and DBB

projects are similar to their mean values. These results also indicated that both the DB

and DBB contractors were bidding below the estimated costs, however, the DB

contractors were bidding the contract below the DBB contractors.

In addition, the results indicate that the mean construction cost growth of DB projects

(16.9%) is higher than that of DBB projects (11.5%). The median values for both DB and

DBB projects are similar to their mean values. This indicates that the DB projects were

experiencing higher construction cost growth than the DBB projects.

The mean Total Cost Growth of DB projects (3.1%) is lower than that of DBB

projects (8.1%). The median values for both DB and DBB projects are less than their

mean values. This indicates that the DB projects had lower total cost growth than the

DBB projects.

The mean Cost per Square Foot of DB projects ($416/SF) is higher than that of DBB projects ($409/SF). The median values for both DB and DBB projects are less than their mean values. These results indicate that the DB projects had a higher Cost per Square

Foot than that of the DBB projects.

Table 7. Descriptive Statistics of Cost Metrics.

Design-Build Projects (N= 42) Design-Bid-Build Projects (N= 42) No. Cost Metrics Mean Median Standard Mean Median Standard Deviation Deviation Contract Award -11.1 -10.9 12.6 -2.8 -1.0 13.5 1 Cost Growth (%) Construction Cost 16.9 15.1 16.2 11.5 8.0 9.2 2 Growth (%) Total Cost 3.1 -1.4 16.6 8.1 5.6 15.8 3 Growth (%) Cost Per Square 416 375 267 409 354 260 4 Foot ($/SF)

Table 8 shows the mean, median, and standard deviation of schedule performance metrics. The results indicate that the mean Design and Construction Schedule Growth of

DB projects (-5.3%) is lower than that of DBB projects (7.3%). The median values for both DB and DBB projects are lower than their mean values. It showed that the DB projects were experiencing approximately 2.5 times less Design and Construction

Schedule Growth than the DBB projects.

The results showed that the mean Total Schedule Growth of DB projects (-3.7%) is lower than that of DBB projects (28.6%). The median values for DB and DBB are lower than their mean values. These results indicate that the DB projects were experiencing approximately four times less Total Schedule Growth than the DBB projects.

The mean Construction Intensity of DB projects (203 SF/Day) is higher than that of

DBB projects (75 SF/Day). The median values for the DB and DBB projects are less than

their mean values. These results indicate that the DB projects were completed

approximately three times faster than the DBB projects.

Table 8. Descriptive Statistics of Schedule Metrics.

Design-Build Projects (N= 42) Design-Bid-Build Projects (N= 42) No. Schedule Metrics Standard Standard Mean Median Mean Median Deviation Deviation Design and Construction -5.3 -8.6 16.4 7.3 5.4 8.2 1 Schedule Growth (%) Total Schedule -3.7 -8.6 19.8 28.6 14 57 2 Growth (%) Construction 203 127 342 75 60 61 3 Intensity (SF/Day)

Table 9 shows the mean, median, and standard deviation of Change-Order Cost performance metrics. The results show that the mean Design Change-Order Cost Growth of DB projects (1.3%) is lower than that of DBB projects (2.1%). The median values for

DB projects are 0% and 1.6% for DBB projects. This indicates that the DB projects had less Design Change-Order Cost Growth than that for the DBB contractors.

The results indicate that the mean Construction Change-Order Cost Growth of DB projects (1.6%) is lower than that of DBB projects (5.7%). The median values for both

DB and DBB projects are less than their mean values. This result indicates that the DB projects had approximately 3.5 times less Construction Change-Order Cost Growth than that of the DBB projects.

The mean Total Change-Order Cost growth of DB projects (2.3%) is lower than that of DBB projects (7.7%). The median value for DB projects is similar to its mean value.

However, the median value of DBB projects is less than the mean value. It showed that the DB projects had approximately three times less Total Change-Order Cost Growth than that of the DBB projects.

Table 9. Descriptive Statistics of Change-Order Cost Metrics.

Design-Build Projects (N= 42) Design-Bid-Build Projects (N= 42) No. Cost Metrics Standard Standard Mean Median Mean Median Deviation Deviation Design Change- 1 Order Cost 1.3 0.0 2.4 2.1 1.6 1.6 Growth (%) Construction 2 Change-Order 1.6 0.0 2.4 5.7 4.6 4.6 Cost Growth (%) Total Change- 3 Order Cost 2.3 2.0 3.9 7.7 6.0 5.0 Growth (%)

5.2 One-way Analysis of Variance

One-way Analysis of Variance (ANOVA) was conducted to determine whether the DB projects outperformed the DBB projects in terms of cost, schedule, and change orders. To conduct this test, the following four assumptions must be met: 1) the sample should be randomly selected or by means of a convenient random sampling, 2) the dependent variables should be in interval or ratio scale, 3) the dependent variables should be normally distributed, and 4) the variances of the two groups should be equal.

The first assumption is that the factorial ANOVA requires the observations to be mutually independent of each other. The data should be randomly selected or by means 51

of a convenient random sampling, which is true in this case. The questionnaires were sent

out randomly all over the United States to collect the data.

The second assumption requires that the dependent variable should be in either a ratio

scale or an interval scale. Similarly, the independent variable should be in a nominal

scale. If the independent variables are not nominal, they need to be grouped first before

the factorial ANOVA can be done. In this case, all the dependent variables that are

performance metrics are in the ratio scale. The independent variable in this study is a

project delivery type that is in the nominal scale.

The third assumption is that ANOVA assumes that the dependent variable

approximates a normal distribution. This assumption can be verified either by checking

histograms or by the Anderson-Darling test. The histograms and test results are shown in

the Section 4.3.

The fourth assumption is that the factorial ANOVA assumes that the variances of the

two groups are equal. Levene’s test was conducted to test this assumption. The results of

this test are described in the following sections.

5.3 Normality Assumptions Test Results

One of the main assumptions of the ANOVA test is that the data should be normally

distributed. The Anderson Darling Test is conducted to check whether the data are

normally distributed. The null hypothesis of this test is that the data are normally

distributed. If the p value is less than 0.05, it shows that the data distribution is not normal.

Normality needs to be verified in order to be used in the one-way ANOVA test. In order to obtain this information, a histogram was created from the SPSS software 52

program for each performance metric. For verification purposes, Anderson-Darling tests

were also performed.

Figure 12 shows the histograms for Contract Award Cost Growth for DB and DBB

projects. The graphs follow a normal distribution, with a slight skew to the left. The DBB

curve skews slightly more to the left than the DB curve. The Anderson darling test was

performed to determine whether the data follows the normal distribution.

Figure 12. Histograms of Contract Award Cost Growth.

Table 10 shows the results of the Anderson Darling test, indicating that the Contract

Award Cost Growth data in both DB and DBB projects were normally distributed

because the p value is higher than 0.05. Even though the nomality graph did not show that the data were normally distributed, the Anderson Darling test showed otherwise.

Table 10. Anderson Darling Test for Contract Award Cost Growth. Performance Metrics Statistics p Value DB Contract Award Cost Growth 0.40 0.368 DBB Contract Award Cost Growth 0.72 0.058

Figure 13 shows the histograms for Construction Cost Growth for DB and DBB

projects. In this case as well, the graph follows a normal distribution with a slight skew to

the left. The DB distribution curve resembles more normality than the DBB curve. The

Anderson Darling test was performed to verify numerically whether the data follows a

normal distribution.

Figure 13. Histograms of Construction Cost Growth.

Table 11 shows the results of the Anderson Darling test, indicating that the

Construction Cost Growth data in DB and DBB projects were not normally distributed

because the p value is lower than 0.05. Results of this test rejects the null hypothesis that the data is normally distrubuted. However, the ANOVA test is a robust test and the violation of the normality will not affect the test results if the sample is large (> 30 samples).

Table 11. Anderson Darling Test for Construction Cost Growth. Performance Metrics Statistics p Value DB Construction Cost Growth 1.40 <0.001* DBB Construction Cost Growth 3.27 <0.001*

*Significant at alpha level 0.05 54

Figure 14 shows the histograms for Total Cost Growth for both DB and DBB

projects. The Total Cost Growth follows a normal distribution with a slight skew to the

left. These two normality curves are similar to the two curves presented in Figure 1. The

Anderson Darling test was performed to determine numerically whether the data follows

the normal distribution.

Figure 14. Histograms of Total Cost Growth.

Table 12 shows the results of Anderson Darling test, indicating that the Total Cost

Growth data in DB projects were not normally distributed because the p value is lower than 0.05. It rejects the null hypothesis that the data is normally distrubuted. However, the ANOVA test is a robust test and the violation of the normality will not affect the test results if the sample is large (> 30 samples).The results indicate that the Total Cost

Growth data in DBB projects were normally distributed because the p value is higher than 0.05. Even though the nomality graph did not that show the data were normally distributed, the Anderson Darling test showed otherwise.

Table 12. Anderson Darling Test for Total Cost Growth. Performance Metrics Statistics p Value DB Total Cost Growth 2.60 <0.001*

DBB Total Cost Growth 0.67 0.082 *Significant at alpha level 0.05

Figure 15 shows the histograms for Cost Per Square Foot for DB and DBB projects.

The Cost Per Square Foot follows a normal distribution with a slight skew to the left to approximately the same degree for both DB and DBB projects. Since the Cost Per Square

Foot does not follow the normal distribution curve, the Anderson Darling test was performed to determine numerically whether the data follows the normal distribution.

Figure 15. Histograms of Cost Per Square Foot.

Table 13 shows the results of Anderson Darling test, indicating that the Cost per

Square Foot data in DB and DBB projects were not normally distributed because the p value is lower than 0.05. It rejects the null hypothesis that the data is normally distrubuted. However, the ANOVA test is a robust test and the violation of the normality will not affect the test results if the sample is large (> 30 samples).

Table 13. Anderson Darling Test for Cost Per Square Foot Performance Metrics Statistics p Value DB Cost Per square Foot 3.22 <0.001*

DBB Cost Per Square foot 1.58 <0.001* *Significant at alpha level 0.05

Figure 16 shows the histograms for Design and Construction Schedule Growth for

DB and DBB projects. The graph follows a normal distribution with a slight skew to the left. Since the Design and Construction Schedule Growth does not follow the normal distribution curve, the Anderson Darling test was performed to determine numerically whether the data follows normal distribution.

Figure 16. Histogram of Design and Construction Schedule Growth.

Table 14 shows the results of the Anderson Darling test, indicating that the Design and Construction Schedule Growth data in DB and DBB projects were not normally distributed because the p value is lower than 0.05. It rejects the null hypothesis that the data is normally distrubuted. However, the ANOVA test is a robust test and the violation of the normality will not affect the test results if the sample is large (> 30 samples).

Table 14. Anderson Darling Test for Design and Construction Schedule Growth. Performance Metrics Statistics p Value DB Design and Construction Schedule Growth 1.73 <0.001*

DBB Design and Construction Schedule Growth 1.39 <0.001* *Significant at alpha level 0.05 Figure 17 shows the histograms for the Total Schedule Growth. The graph follows a normal distribution with a slight skew to the left. The DB curve skews more to the left, and the DBB curve is close to normal. Since Total Schedule Growth does not follow the normal distribution curve, the Anderson Darling test was performed to determine numerically whether the data follows normal distribution.

Figure 17. Histograms of Total Schedule Growth.

Table 15 shows the results of the Anderson Darling test, indicating that the Total

Schedule Growth data in DB and DBB projects were not normally distributed because the p value is lower than 0.05. It rejects the null hypothesis that the data is normally distrubuted. However, the ANOVA test is a robust test and the violation of the normality will not affect the test results if the sample is large (> 30 samples).

Table 15. Anderson Darling Test for Total Schedule Growth. Performance Metrics Statistics p Value DB Total Schedule Growth 2.74 <0.001*

DBB Total Schedule Growth 6.38 <0.001* *Significant at alpha level 0.05

Figure 18 shows the histograms for the Construction Intensity (SF/Day). The graph

follows a normal distribution with skewness to the left in both the DB and DBB projects.

Since the Construction Intensity does not follow the normal distribution curve, the

Anderson Darling test was performed to determine numerically whether the data follows

the normal distribution.

Figure 18. Histograms of Construction Intensity.

The results of Anderson Darling test shown in Table 16 indicate that the Construction

Intensity of DB and DBB projects were not normally distributed because the p value is lower than 0.05. It rejects the null hypothesis that the data is normally distrubuted.

However, the ANOVA test is a robust test and the violation of the normality will not affect the test results if the sample is large (> 30 samples).

Table 16. Anderson Darling Test for Construction Intensity (SF/Day). Performance Metrics Statistics p Value DB Construction Intensity (SF/Day) 7.38 <0.001*

DBB Construction Intensity (SF/Day) 3.27 <0.001* *Significant at alpha level 0.05

Figure 19 shows the histograms for the Design Change-Order Cost Growth. The graph follows a normal distribution with a slight skew to the left. The DBB curve skews more to the left than the DB curve, which appears to be close to normal. Since the Design

Change-Order Cost Growth does not follow the normal distribution curve, the Anderson

Darling test was performed to determine numerically whether the data follows the normal distribution.

Figure 19. Histogram of Design Change-Order Cost Growth.

Table 17 shows the results of Anderson Darling test indicating that the Design

Change-Order Cost Growth data for DB projects were not normally distributed because the p value is lower than 0.05. It rejects the null hypothesis that the data is normally distrubuted. However, the ANOVA test is a robust test and the violation of the normality

will not affect the test results if the sample is large (> 30 samples).The results showed the

Design Change-Order Cost Growth data for DBB projects were normally distributed because the p value is higher than 0.05. The nomality graph did not show that the data were normally distributed; however, the Anderson Darling test showed otherwise.

Table 17. Anderson Darling Test for Design Change-Order Cost Growth. Performance Metrics Statistics p Value DB Design Change-Order Cost Growth 3.61 <0.001*

DBB Design Change-Order Cost Growth 0.45 0.274 *Significant at alpha level 0.05

Figure 20 shows the histograms for the Construction Change-Order Cost Growth. The graph follows a normal distribution with a slight skew to the left. Since the Construction

Change-Order Cost Growth does not follow the normal distribution curve, the Anderson

Darling test was performed to determine numerically whether the data follows the normal distribution.

Figure 20. Histogram of Construction Change-Order Cost Growth.

Table 18 shows the results of Anderson Darling test, indicating that the Construction

Change-Order Cost Growth data in DB and DBB projects were not normally distributed because the p value is lower than 0.05. It rejects the null hypothesis that the data is normally distrubuted. However, the ANOVA test is a robust test and the violation of the normality will not affect the test results if the sample is large (> 30 samples).

Table 18. Anderson Darling Test for Construction Change-Order Cost Growth Performance Metrics Statistics p Value DB Construction Change-Order Cost Growth 5.02 <0.001*

DBB Construction Change-Order Cost Growth 3.14 <0.001* *Significant at alpha level 0.05

Figure 21 shows the histograms for the Total Change-Order Cost Growth. The graph follows a normal distribution with a slight skew to the left. Since the Total Change-Order

Cost Growth does not follow the normal distribution curve, the Anderson Darling test was performed to determine whether the data follows the normal distribution.

Figure 21. Histogram of Total Change-Order Cost Growth.

Table 19 shows the results of Anderson Darling test, indicating that the Total Change-

Order Cost Growth data in DB and DBB projects were not normally distributed because the p value is lower than 0.05. These results reject the null hypothesis that the data is normally distrubuted. However, the ANOVA test is a robust test and the violation of the normality will not affect the test results if the sample is large (> 30 samples).

Table 19. Anderson Darling Test for Total Change-Order Growth. Performance Metrics Statistics p Value DB Total Change-Order Cost Growth 1.86 <0.001*

DBB Total Change-Order Cost Growth 1.87 <0.001* *Significant at alpha level 0.05

4.4 Results of Equal Variance Test

Levene’s test was conducted to check the homogeneity of variance in DB and DBB

projects. The null hypothesis for this test is that the variances of these two groups are

equal. If the p value is less than 0.05, then the null hypothesis of equal variances is

rejected.

Table 20 shows the Levene statistic of cost metrics. All the cost metrics except the

Construction Cost Growth metric has a p value of more than 0.05. Therefore, the variance

of the Construction Cost Growth metric in DB and DBB projects is not equal. To

overcome the violation of this assumption, the means for the Construction Cost Growth

of these two groups should be statistically compared by using the t-test, assuming

unequal variance.

Table 20. Results of Homogeneity of the Variance Test for Cost Metrics.

Performance Metrics Levene Statistic p value Contract Award Cost Growth 0.01 0.911 Construction Cost Growth 17.84 <0.001* Total Cost Growth 0.01 0.980 Cost Per Square Foot 0.46 0.457 * Significant at alpha level 0.05

Table 21 shows the results of Levene’s tests for schedule metrics. The null hypothesis

for this test is that the variances of these groups are equal. If the p value is less than 0.05,

then the null hypothesis of equal variances is rejected. All the schedule metrics have a p

value less than 0.05. Therefore, the variances of all schedule growth metrics in these two

groups of projects are not equal. To overcome the violation of this assumption, the means

of these three metrics should be statistically compared using the t-test, assuming unequal

variance.

Table 21. Results of Homogeneity of the Variance Test for Schedule Metrics

Performance Metrics Levene Statistic p value Design and Construction Schedule Growth 4.47 0.037* Total Schedule Growth 4.58 0.035* Construction Intensity 4.73 <0.001* * Significant at alpha level 0.05

Table 22 shows the results of Levene’s tests for Change-Order Cost metrics. The null

hypothesis for this test is that the variances of these groups are equal. If the p value is less than 0.05, then the null hypothesis of equal variances is rejected. All the Change-Order

Metrics, except for the Construction Change-Order Growth metric, have p values of more

than 0.05. Therefore, the variance of Construction Change-Order Cost Growth in DB and 64

DBB projects is not equal. To overcome the violation of this assumption, the means for

the Construction Change-Order Cost Growth of these two groups should be statistically

compared using the t-test, assuming equal variance.

Table 22. Results of Homogeneity of the Variance Test for Change Order Metrics.

Performance Metrics Levene Statistic p value Design Change-Order Cost Growth 3.75 0.056 Construction Change-Order Cost Growth 10.73 0.002* Total Change-Order Cost Growth 1.67 0.200 * Significant at alpha level 0.05

The results of ANOVA test for cost metrics, shown in Table 23, indicate that only the

Contract Award Cost Growth mean is significantly different between DB and DBB

projects. The results also indicated that the mean Contract Award Cost Growth of DBB

projects are significantly higher than that of DB projects.

Table 23. ANOVA Results for Cost Metrics. No. Cost Metrics DB Mean DBB Mean Test Critical p Value (N=42) (N=42) Statistic Values 1 Contract Award Cost -11.1 -2.8 8.48 3.96 <0.001* Growth (%) 2 Construction Cost 16.9 1.15 1.86 1.99 0.067 Growth (%) 3 Total Cost Growth (%) 3.1 8.5 1.99 3.96 0.162 4 Cost Per Square Foot 416 409 0.02 3.96 0.902 ($/DAY) * Significant at alpha level 0.05

The box plots of the cost performance metrics, shown in Figure 22, indicate that there

are higher outliers for the Total Cost Growth metrics than for the other two metrics.

Contract Award Cost Growth has just one outlier in DBB projects. There are a few

outliers for Construction Cost Growth of DBB projects. Cost Per Square Foot has just 65

two outliers in DB projects and two outliers in DBB projects. However, in Total Cost

Growth, both DB and DBB projects have a number of outliers.

Figure 22. Box Plots of Cost Performance Metrics.

Table 24 shows the results of the ANOVA test for schedule metrics. The assumption of equal variances was rejected by all three performance metrics. Therefore, a t-test with unequal variances was conducted to find the statistically significant difference. The results of this test showed that the means for Design and Construction Schedule Growth,

Total Schedule Growth, and Construction Intensity are significantly different between

DB and DBB projects. The results indicate that the means for Design and Construction

Schedule Growth and Total Schedule Growth of DBB are significantly higher than that of

DB projects. In addition, the mean for Construction Intensity of DBB projects is

significantly lower than for DB projects.

Table 24. T-test for Unequal Variance Results for Schedule Metrics No. Schedule Metrics DB Mean DBB Mean Test Critical p Value (N=42) (N=42) Statistic Values 1 Design and -5.28 7.3 4.45 2.00 <0.001* Construction Schedule Growth (%) 2 Total Schedule Growth -3.7 28.6 3.47 2.00 <0.001* (%) 3 Construction Intensity 203 75 2.39 2.01 0.021* (SF/DAY) * Significant at alpha level 0.05

The box plots for the schedule performance metrics, shown in Figure 23, indicate that

there are higher outliers in the Construction Intensity metrics than in the other two

metrics. Design and Construction Schedule Growth has two outliers for DB projects.

There are three outliers in DB Total Schedule Growth and two in DBB Total Schedule

Growth. However, in the Construction Intensity metrics, both DB and DBB projects have

a number of outliers.

Figure 23. Box Plots of Schedule Performance Metrics

Table 25 shows the results of the ANOVA and t-test for Change-Order Cost metrics.

The ANOVA test was conducted for the Design Change-Order Cost Growth and Total

Change-Order Cost Growth to determine whether their means were significantly different. However, for Construction Change-Order Cost Growth, since the variances of these groups were not equal, a t-test for unequal variances was conducted. The results showed that the means for the Construction Change-Order Cost Growth and Total

Change-Order Cost Growth are significantly lower in DB than in DBB projects.

Table 25. ANOVA and t-test for Unequal Variance Results of Change-Order Cost Metrics. No. Change Order DB Mean DBB Mean Test Critical p Value Metrics (N=42) (N=42) Statistic Values 1 Design Change-Order 1.3 2.1 3.07 3.96 0.08 Cost Growth (%) 2 Construction Change- 1.6 5.7 5.03 1.99 <0.001* Order Cost Growth (%) 3 Total Change-Order 2.3 7.7 23.69 3.96 <0.001* Cost Growth (%) * Significant at alpha level 0.05

The box plots of the change-order cost growth metrics, shown in Figure 24, indicate that there are a greater number of outliers in Construction Change-Order Cost Growth than in the other two metrics. Design Change-Order Cost Growth has three outliers in DB projects and no outliers in DBB. There are two outliers in Construction Change-Order

Cost Growth for DB projects and six outliers in DBB projects. There are two outliers in

Total Change-Order Cost Growth for DB projects and five outliers for DBB projects.

Figure 24. Box Plots of Change-Order Cost Performance Metrics

CHAPTER 6

CONCLUSION AND RECOMMENDATIONS

6.1 Conclusions

This thesis has collected data, by means of convenient random sampling, and analyzed two similar types of DB and DBB projects recently built by universities within the U.S.

All the projects were used for building classrooms, offices, or laboratories. All the projects were administered by similar construction departments established within the university systems. The samples are large enough, with 42 DB projects and 42 DBB projects. These two project types are unique since they are newly constructed buildings on an operating and occupied university campus; therefore, care should be taken while interpreting these results for other types of university structures (parking lots or football fields), tenant improvement buildings (classroom renovation), or such projects as shopping malls or a public library.

6.1.1 Cost Growth

This study analyzed the cost growth in four separate categories: Contract Cost Growth,

Construction Cost Growth, Total Cost Growth, and Cost Per Square Foot. The results showed that only the mean Contract Award Cost Growth of DB projects is significantly lower than that of DBB projects. The data also showed that DB projects had a higher

Construction Cost Growth and a higher Cost Per Square Foot than DBB projects; however, that finding was not found to be statistically significant. The Total Cost Growth data showed that DB projects had a lower Total Cost Growth, but this result also was found to be statistically insignificant.

6.1.2 Schedule Growth

This study analyzed the three categories of project schedule growth: Design and

Construction Schedule Growth, Total Schedule Growth, and Construction Intensity. The results showed that the means of Design and Construction Schedule Growth, Total

Schedule Growth, and Construction Intensity were significantly different in DB projects than that of DBB projects. The results also showed that the mean Design and

Construction Schedule Growth and the mean Total Schedule Growth of DB projects were significantly lower than that of DBB projects. In addition, the mean Construction

Intensity of DB projects were significantly higher than that of DBB projects.

6.1.3 Change Order Growth

This study analyzed change-order cost growth in three separate categories: Design

Change Order Growth, Construction Change Order Growth, and Total Change Order

Growth. The results showed that the means of Construction Change Order Cost Growth and Total Change Order Cost Growth was significantly lower in DB projects than that of

DBB projects. The results also showed that the mean of Design Change-Order Cost

Growth of DB projects were lower than that of DBB projects; however, these results were not found to be statistically significant.

For this research project, a comprehensive questionnaire was developed for ease of data collection for this study and for future studies as well. Obstacles and barriers existed while using this questionnaire; for future studies, it is recommended that the questionnaire be shortened to allow for a better response rate. Furthermore, this study can be a valuable asset to the construction industry in the university environment as well as the industry as a whole because different research outcomes of DB and DBB delivery 72

methods were evaluated, analyzed, and interpreted. The results of this research will enable owners in the university environment as well as across the industry to become more familiar with comparisons between the DB and DBB delivery methods, which will enable them to logically choose which delivery method is appropriate for use on a project–by-project basis.

6.2 Recommendations for Further Study

The following recommendations are suggested for further research:

1. The data collected for this study consisted of 42 samples of DB and 42 samples of

DBB. To justify the findings of this study, it is reccommended to conduct the

study with a larger sample size.

2. This study was spread across the United States but received completed

questionnaires from only 11 states. It is recommended that future surveys receive

completed questionnaires from every state in order to evaluate that data

appropriately and increase external validity.

3. Once DB is widely used in the university system, it is reccommended that data be

evaluated by regional territories, such as North, South, East, and West to

determine if location has an effect on the delivery method.

REFERENCES

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Engineering News Record (2011) “Building Cost Index.” Design and Construction Resources , McGraw Hill Construction, 2011 Edition.< http://enr.construction.com/economics/ > access on March, 2011

Erne, J.J., Schexnayder, C., and Flora, G. (1999). “Design build effects on a construction company.” Transportation Research Record 1654 181-187.

Federal Construction Council. (1993). “Experiences of federal agencies with the design build approach to construction.” Tech. Rep. 122 , Consulting Com. On Cost Accounting, National Academy Press, Washington, D.C.

Gransberg, D.D., Senadheera S.P.(1999). “Design build contract award methods for transportation projects.”J. Mgmt. Engrg ., 20 (4) 162-169

Hale,D., Shrestha,P. P., Gidson,G.E. Jr., and Migliaccio, G.C. (2009) “Empirical comparison of design build and design bid build project delivery methods.”J. Construc. Eng. Manage., 135 (7) pp 579-587

Ibbs,W. C., Kwak, Y.H. , Ng,T., and Odabasi,A. M.. (2003). “Project delivery systems and project change: Quantitative analysis.”J. Construc. Eng. Manage., 129 (4), 382-388

Konchar,M., and Sanvido,V. (1998). “Comparison of U.S. project delivery systems.” J. Construc. Eng. Manage., 124 (6) 435-445

Levine et al. (2007). “Statistics for managers.” Pearson Prentice Hall, Upper Saddle River, New Jersey, 8-10, 124-128, 422-432

Ling,F. Y. Y., Chan,S.L., Chong,E., and Ee,L.P.. (2004) “Predicting performance of design build and design bid build projects.” J. Construc. Eng. Manage., 130 (1) 75-83

Molenaar, K., Songer,A., and Barash, M.. (1999). “Public sector design build evolution and performance.” J. Construc. Eng. Manage., 15 (2) 54-62

Myers, J.J. (1994) “Rep. on Design-Build as an Alternate Delivery Method for Public Owners. ”Com. on Mgmt and Contracting Alternatives , Building Futures Council, Georgetown, Md.

Scott, S., Molenaar, K., Gransberg, D., and Smith, N. (2006). “Best-value procurement methods for highway construction projects.” Rep. No. 561 , Project No. 10-61, NCHRP, Transportation Research Board, National Research Council, Washington D.C. 74

Shrestha, P.P., O’Connor, J.T., Gibson Jr., G.E. (2010) “Performance comparison of large design build and design bid build highway Projects.” J. Construc. Eng. Manage., in print.

Wardani,M.A. El., Messner,J.I., and Horman,M.J.. (2006). “Comparing procurement methods for design build projects.” J. Construc. Eng. Manage., 132 (3) 230-238

Warne, T.R. (2005) “Design build contracting for highway projects: A performance assessment,” Tom Warne & Associates, LLC, May 2005.

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APPENDIX A

BENCHMARKING OF DB FOR UNIVERSITY PROJECTS

QUESTIONNAIRE SURVEY

We would like to thank you in advance for the time and effort involved in your agency’s participation in this research.

This interview guide is divided into four sections; Project General Information; Project Characteristics; Project Performances; and Stakeholders’ Success. If not enough space is provided for the brief questions, please feel free to attach extra sheets to the document.

In the questions, we ask for detailed information on project characteristics and performance. Please do what you can to assemble this information as fully as possible. Your detailed responses will allow us to understand to what extent these project characteristics and performance measurements affect the benchmarking of University projects.

The confidentiality of this interview will be maintained. This interview data will not be placed in any permanent record, and will be destroyed when no longer needed by the researchers. The identity of person who provided all this information will remain anonymous. The data obtained during this interview will not be linked in any way to participants’ names.

Please return this questionnaire via email, or by mail to the following address: James D. Fernane Construction Project Manager/Graduate Student The University of Nevada Las Vegas 7069 Harbor View Dr Las Vegas, NV 89119 Email: [email protected]

Section 1:

1 Project General Information 1.1 Name of Owner Organization: ______1.2 Name of Project:______1.3 Project ID:______1.4 Project Description:______1.5 Project Site Location:______1.6 Contact Person (Name of person filling this questionnaire):______1.7 Contact Person’s Phone:______1.8 Contact Person’s Fax: ______1.9 Contact Person’s Email Address: ______1.10 Contact Person’s Role / Title in this Project: ______1.11 Date of Assessment: ______

Section 2: 2 Project Characteristics 2.1 Current State of Project 2.1.1 Describe current state of this project. Substantial Completion on ______OR % of completion ______Current planned completion date ______

2.2 Project Scope Please provide following project data. 2.2.1 Total Square Feet ______2.2.2 Total Stories ______2.2.3 Type of Construction ______

2.3 Project Calendar 2.3.1 Please fill the start and end dates (month / year) of different phases of this project. Project phases Date in months & years Total project Star Design Construction

Section 3: 3 Project Performance: 3.1 Project Cost Related Performance: Please provide the following cost related performance data of your project.

No. Cost related project performance Cost (US $)

1. Owner estimated design and construction cost

2. Contractor’s bid / negotiated amount

3. Contract amount

4. Total project completion cost

5. Owner estimated design cost

6. Final design cost

7. Owner estimated construction cost

8. Final construction cost (including change orders)

3.2 Project Schedule Related Performance: Please provide the following schedule-related performance data of this project.

No. Schedule related project performance Duration (Days or Months)

1. Owner estimated design and construction duration

2. Contractor’s bid duration

3. Actual project completion duration

4. Owner estimated design duration

5. Final design duration

6. Owner estimated construction duration

Contractors schedule duration at NTP. (What was 7. the Contractors original number of days to complete)

8. Final construction duration

3.3 Project Change Order- Related Performance: Please provide the following change order-related performance data of this project.

No. Change order-related project performance

1. Total number of design change orders

2. Total cost of design change orders (US$)

3. Total number of construction change orders

4. Total cost of construction change orders (US$)

Section 4: 4 Stakeholders’ Success: 4.1 Who was the design-build contractor for this project? Please provide the following information. Name of Contractor: ______Website address (If any): ______Email Address ______Phone Number______

4.2 How would you rate the overall performance of this project compared to other design-build (DB) projects? Excellent Good Fair Poor

APPENDIX B

BENCHMARKING OF DBB FOR UNIVERSITY PROJECTS

QUESTIONNAIRE SURVEY

We would like to thank you in advance for the time and effort involved in your agency’s participation in this research.

Please return this questionnaire via email, by fax, or by mail to the following address: James D Fernane Construction Project Manager/Graduate Student The University of Nevada, Las Vegas 7069 Harbor View Dr Las Vegas, NV89119 Email: [email protected]

Section 1: 5 Project General Information 5.1 Name of Owner Organization: ______5.2 Name of Project: ______5.3 Project ID: ______5.4 Project Description:______5.5 Project Site Location:______5.6 Contact Person (Name of person filling this questionnaire): ______5.7 Contact Person’s Phone: ______5.8 Contact Person’s Fax: ______5.9 Contact Person’s Email Address: ______5.10 Contact Person’s Role / Title in this Project: ______5.11 Date of Assessment: ______

Section 2: 6 Project Characteristics 6.1 Current State of Project 6.1.1 Describe current state of this project. Completed on ______Operational from ______OR % of completed ______Current planned completion date ______

6.2 Project Scope Please provide following project data. 6.2.1 Total Square feet ______6.2.2 Total Stories ______6.2.3 Type of construction ______

6.3 Project Calendar 6.3.1 Please fill the start and end dates (month / year) of different phases of this project. (Changed from DB Questionnaire) Project phases Date in months & years Design

Contract

Procurement

Construction

Section 3: 7 Project Performance: 7.1 Project Cost Related Performance : Please provide the following cost related performance data of your project.

No. Cost related project performance Cost (US $)

1. Owner estimated design cost

2. Actual design cost

3. Owner estimated construction cost

4. Contractor’s bid / negotiated amount

5. Construction contract amount

6. Final design cost

9. Final construction cost (including change orders)

7.2 Project Schedule Related Performance: Please provide the following schedule-related performance data of this project.

No. Schedule related project performance Duration (Days or Months)

1. Owner estimated design duration

2. Actual design duration

3. Owner estimated construction duration

4. Contractor’s bid duration

Contractors schedule duration at NTP. (What was 5. the Contractors original number of days to complete)

6. Final construction duration

7.3 Project Change Order- Related Performance: Please provide the following change order-related performance data of this project.

No. Change order-related project performance

1. Total number of design change orders

2. Total cost of design change orders (US$)

3. Total number of construction change orders

4. Total cost of construction change orders (US$)

Section 4: 8 Stakeholders’ Success: 8.1 Who was the contractor for this project? Please provide the following information. Name of Contractor: ______Website address (If any): ______Email Address: ______Phone Number: ______

8.2 How would you rate the overall performance of this project compared to other design-bid-build (DBB) projects? Excellent Good Fair Poor

APPENDIX C

Cost Data for DB and DBB Projects

Serial Number Project Delivery Estimated Design Cost Bid Design Cost Final Design and Location Method Cost 01 CA DB Incld in DB Incld in DB estimate Incld in DB contract contract 02 AZ DB 1,550,000 Incld in DB contract 1,485,000 03 AZ DB 2,500,000 Incld in DB contract 2,395,450 04 AZ DB 1,850,000 Incld in DB contract 1,800,000 05 ND DB 1,250,000 Incld in DB contract 1,100,000 06 OK DB Incld in DB Incld in DB estimate Incld in DB contract contract 07 OK DB 400,000 Incld in DB contract 410,000 08 OK DB 166,882 Incld in DB contract 286,020 09 NV DB 13,432,000 Incld in DB contract 13,821,000 10 CA DB 2,300,000 Incld in DB contract 2,500,000 11 CA DB 4,100,000 Incld in DB contract 3,766,000 12 CA DB 5,000,000 Incld in DB contract 5,062,000 13 CA DB 2,900,000 Incld in DB contract 2,685,000 14 FL DB 1,011,000 Incld in DB contract 907,000 15 CA DB 8,000,000 Incld in DB contract 8,000,000 16 MI DB 801,000 Incld in DB contract 562,000 17 CA DB 1,907,000 Incld in DB contract 1,697,000 18 CA DB 1,071,000 Incld in DB contract 745,879 19 CA DB 1,500,000 Incld in DB contract 1,498,447 20 CA DB 1,772,000 Incld in DB contract 1,004,440 21 CA DB 3,477,932 Incld in DB contract 1,941,837 22CA DB 371,000 Incld in DB contract 365,500 23 CA DB 1,234,000 Incld in DB contract 1,100,000 24 CA DB 1,043,000 Incld in DB contract 874,852 25 MI DB 350,000 Incld in DB contract 359,670 26 AZ DB 2,000,000 Incld in DB contract 2,283,157 27 AZ DB 965,000 Incld in DB contract 885,650 28 WY DB Incld in DB Incld in DB estimate Incld in DB contract contract 29 WY DB Incld in DB Incld in DB estimate Incld in DB contract contract 30 CO DB Incld in DB Incld in DB estimate Incld in DB contract contract 31 AZ DB 12,000,000 Incld in DB contract 9,876,650

32 AZ DB 8,000,000 Incld in DB contract 7,575,000 33 AZ DB 2,300,000 Incld in DB contract 1,900,000 34 AZ DB 3,500,000 Incld in DB contract 3,475,000 35 AZ DB 1,000,000 Incld in DB contract 985,000 36 ND DB Incld in DB Incld in DB estimate Incld in DB contract contract 37 DB 203,000 Incld in DB contract 149,283 38 CA DB 2,057,500 Incld in DB contract 1,980,950

Serial Number Project Delivery Estimated Design Cost Bid Design Cost Final Design and Location Method Cost 39 CA DB Incld in DB estimate Incld in DB contract 806,398 40 CA DB 950,000 Incld in DB contract 936,659 41 CA DB 500,000 Incld in DB contract 400,000 42 CA DB 817,000 Incld in DB contract 673,740 43 WI DBB 475,000 440,000 465,000 44 WI DBB 800,000 750,000 778,007 45 WI DBB 1,500,000 1,800,000 1,914,000 46 AZ DBB 375,000 355,000 425,000 47 AZ DBB 350,000 395,000 434,000 48 AZ DBB 825,000 810,000 855,310 49 AZ DBB 414,250 415,000 431,161 50 WY DBB 98,000 100,000 103,638 51 NV DBB 1,500,000 803,000 1,300,000 52 NV DBB 200,000 156,000 185,000 53 CA DBB 3,750,000 2,550,555 3,489,056 54 NV DBB 63,000 58,500 69,567 55 WI DBB 2,000,000 1,914,000 2,100,000 56 WI DBB 5,000,000 5,000,000 5,100,000 57 WI DBB 2,000,000 1,867,060 2,001,520 58 WI DBB 8,562,095 8,650,000 8,792,000 59 WI DBB 12,750,000 12,950,000 13,500,000 60 WI DBB 2,250,000 2,280,000 2,431,413 61 CA DBB 673,000 602,561 1,044,903 62 WI DBB 850,000 840,000 925,000 63 CA DBB 1,236,460 1,632,858 1,984,699 64 CA DBB 1,043,000 954,303 1,332,448 65 CA DBB 990,234 582,204 740,571 66 WI DBB 500,000 490,000 506,800 67 CA DBB 461,554 399,710 505,870 68 WI DBB 2,500,000 2,486,950 2,600,000 69 WI DBB 40,000 45,850 45,850 70 NV DBB 95,000 95,000 123,500 71 NV DBB 3,000,000 2,700,000 3,200,000 72 NV DBB 50,000 36,000 39,500 73 CA DBB 522,000 596,557 807,455 74 CA DBB 232,000 168,542 218,759 75 CA DBB 1,091,000 1,319,834 1,727,691 76 MI DBB 657,700 682,700 770,188 77 MI DBB 1,072,809 1,048,000 1,048,000 78 FL DBB 410,000 469,000 469,000 79 CA DBB 1,482,855 1,113,155 1,551,750 80 NV DBB 5,000,000 3,200,000 4,600,000 81 NV DBB 45,000 43,000 45,520 82 NV DBB 10,000,000 8,388,677 8,388,677 83 NV DBB 15,000 11,000 11,000 84 CA DBB 95,000 146,000 190,000

Serial Number Project Delivery Estimated Construction Final and Location Method Construction Cost Contract Construction Cost 01 CA DB Incld in DB estimate 37,070,705 37,606,826 02 AZ DB 20,450,000 20,300,000 20,300,000 03 AZ DB 28,500,000 30,200,000 30,200,000 04 AZ DB 14,650,000 15,874,000 15,874,000 05 ND DB 13,750,000 14,875,500 14,875,500 06 OK DB Incld in DB estimate 2,880,435 2,880,435 07 OK DB 1,600,000 1,897,563 1,897,563 08 OK DB 205,000 272,400 272,400 09 NV DB 13,432,000 13,821,000 13,821,000 10 CA DB 26,403,000 25,497,000 27,837,032 11 CA DB 36,643,180 40,743,180 44,461,835 12 CA DB 45,404,000 50,404,000 51,804,297 13 CA DB 26,146,000 28,997,000 29,853,274 14 FL DB 13,309,000 13,898,000 13,898,000 15 CA DB 60,000,000 60,000,000 60,000,000 16 MI DB 13,350,000 15,478,688 16,040,688 17 CA DB 24,593,000 19,901,701 19,901,701 18 CA DB 18,704,000 16,639,179 16,709,058 19 CA DB 31,241,000 30,434,235 30,831,805 20 CA DB 28,463,946 24,166,179 25,466,266 21 CA DB 47,104,870 47,159,416 47,159,416 22CA DB 3,261,000 2,667,270 2,667,270 23 CA DB 16,507,000 13,381,896 14,521,835 24 CA DB 21,333,600 18,617,452 19,284,530 25 MI DB 5,535,330 4,676,271 5,016,299 26 AZ DB 52,000,000 53,564,244 53,771,146 27 AZ DB 12,247,000 12,000,000 12,726,498 28 WY DB Incld in DB estimate 9,933,000 9,933,000 29 WY DB Incld in DB estimate 1,264,853 1,264,853 30 CO DB Incld in DB estimate 12,829,268 13,002,518 31 AZ DB 110,000,000 103,000,000 103,000,000 32 AZ DB 47,000,000 44,325,000 44,325,000 33 AZ DB 10,700,000 9,278,000 9,278,000 34 AZ DB 29,600,000 32,600,000 32,600,000 35 AZ DB 12,000,000 10,450,000 10,788,150 36 ND DB Incld in DB estimate 3,400,000 3,400,000 37 DB 3,939,720 3,631,003 3,890,063 38 CA DB 42,892,000 40,795,171 41,495,671 39 CA DB 20,249,000 18,849,000 19,036,410 40 CA DB 9,968,000 7,108,756 7,839,935 41 CA DB 4,000,000 3,573,000 3,573,000 42 CA DB Incld in DB estimate 23,749,618 23,749,618 43 WI DBB 5,500,000 4,250,595 5,138,693 44 WI DBB 7,250,000 6,975,999 7,328,800 45 WI DBB 27,000,000 27,895,500 29,056,000 46 AZ DBB 1,950,000 1,925,275 2,634,678 47 AZ DBB 1,500,000 1,855,650 2,366,000 48 AZ DBB 3,750,000 3,975,500 4,267,000 49 AZ DBB 4,807,000 4,839,101 5,117,218 88

Serial Number Project Delivery Estimated Construction Final and Location Method Construction Cost Contract Construction Cost 50 WY DBB 858,100 944,547 1,064,912 51 NV DBB 812,000 906,000 1,108,000 52 NV DBB 2,150,000 1,875,550 2,206,522 53 CA DBB 40,000,000 38,627,000 41,581,677 54 NV DBB 273,800 204,750 210,150 55 WI DBB 24,000,000 27,550,000 29,056,000 56 WI DBB 60,000,000 59,750,000 62,712,631 57 WI DBB 17,000,000 16,500,000 17,562,000 58 WI DBB 85,000,000 95,990,320 100,383,276 59 WI DBB 135,000,000 142,350,950 176,413,000 60 WI DBB 35,000,000 35,375,950 36,492,731 61 CA DBB 14,200,000 14,100,000 17,158,521 62 WI DBB 32,500,000 33,150,975 35,786,294 63 CA DBB 22,779,397 17,292,000 19,033,411 64 CA DBB 21,333,600 17,742,600 18,818,691 65 CA DBB 9,095,157 7,143,600 7,493,680 66 WI DBB 2,500,000 2,485,000 2,646,000 67 CA DBB 9,869,154 8,115,600 8,940,200 68 WI DBB 27,000,000 27,500,000 29,800,000 69 WI DBB 500,000 485,950 515,900 70 NV DBB 950,000 1,332,964 1,420,953 71 NV DBB 2,700,000 2,700,000 3,300,000 72 NV DBB 500,000 388,255 430,020 73 CA DBB 12,204,000 10,199,000 11,040,804 74 CA DBB 2,643,000 2,283,395 2,369,477 75 CA DBB 19,870,000 19,695,000 20,793,260 76 MI DBB 8,756,000 8,756,000 9,700,857 77 MI DBB 9,997,500 9,997,500 11,137,565 78 FL DBB 4,735,000 4,722,000 4,722,000 79 CA DBB 18,559,000 17,450,000 18,581,231 80 NV DBB 3,200,000 3,700,000 4,744,000 81 NV DBB 295,000 295,388 315,044 82 NV DBB 6,500,000 6,968,000 8,004,000 83 NV DBB 40,000 56,200 57,536 84 CA DBB 2,000,000 2,264,072 2,666,960 Serial Number Project Delivery Estimated Design and Contract Design and Final Design and and Location Method Construction Cost Construction Cost Construction Cost 01 CA DB 40,000,000 37,070,705 37,703,278 02 AZ DB 22,000,000 20,300,000 20,300,000 03 AZ DB 31,000,000 30,200,000 30,200,000 04 AZ DB 16,500,000 15,874,000 15,500,650 05 ND DB 15,000,000 14,875,500 14,875,500 06 OK DB 2,900,000 2,880,435 2,880,435 07 OK DB 2,100,000 1,897,563 1,897,563 08 OK DB 225,000 272,400 361,855 09 NV DB 16,467,000 13,821,000 16,659,000 10 CA DB 29,000,000 25,497,000 35,780,000

Serial Number Project Delivery Estimated Design and Contract Design and Final Design and and Location Method Construction Cost Construction Cost Construction Cost 11 CA DB 40,743,292 40,743,180 58,975,000 12 CA DB 50,816,000 50,404,000 66,774,000 13 CA DB 29,046,500 28,997,000 36,416,000 14 FL DB 14,320,000 13,898,000 16,300,000 15 CA DB 90,000,000 60,000,000 90,000,000 16 MI DB 14,151,000 15,478,6880 16,818,453 17 CA DB 26,500,000 19,901,701 25,842,343 18 CA DB 19,775,000 16,639,179 18,975,000 19 CA DB 32,741,000 30,434,235 32,476,156 20 CA DB 30,235,946 24,166,179 28,985,326 21 CA DB 50,582,802 47,159,416 47,530,086 22CA DB 3,632,000 2,667,270 2,667,270 23 CA DB 17,741,000 13,381,896 15,176,582 24 CA DB 22,376,600 18,617,452 19,241,320 25 MI DB 5,895,000 4,676,271 6,235,028 26 AZ DB 54,000,000 53,564,244 56,054,303 27 AZ DB 13,212,000 12,000,000 16,940,712 28 WY DB 8,500,000 9,933,000 10,298,955 29 WY DB 1,250,000 1,264,853 1,297,861 30 CO DB 15,089,756 12,829,268 14,164,501 31 AZ DB 125,000,000 103,000,000 103,000,000 32 AZ DB 55,000,000 44,325,000 53,700,000 33 AZ DB 13,000,000 9,278,000 12,000,000 34 AZ DB 33,100,000 32,600,000 32,600,000 35 AZ DB 13,300,000 10,450,000 12,500,000 36 ND DB 3,550,000 3,400,000 3,400,000 37 DB 4,142,720 3,631,003 4,219,759 38 CA DB 50,225,000 40,795,171 49,555,443 39 CA DB 26,677,716 18,849,000 27,671,930 40 CA DB 10,918,000 7,108,756 10,755,556 41 CA DB 4,500,000 3,573,000 4,562,871 42 CA DB 30,000,000 23,749,618 28,103,799 43 WI DBB 5,975,000 4,690,595 5,603,693 44 WI DBB 8,050,000 7,725,999 8,106,807 45 WI DBB 28,500,000 29,695,500 30,970,000 46 AZ DBB 2,325,000 2,280,275 3,059,678 47 AZ DBB 1,850,000 2,250,650 2,800,000 48 AZ DBB 4,575,000 4,785,500 5,122,310 49 AZ DBB 5,221,250 5,254,101 5,548,379 50 WY DBB 956,100 1,044,547 1,168,550 51 NV DBB 2,312,000 1,709,000 2,408,000 52 NV DBB 2,350,000 2,031,550 2,391,522 53 CA DBB 43,750,000 41,177,554 45,070,732 54 NV DBB 336,800 263,250 279,717 55 WI DBB 26,000,000 29,464,000 31,156,000 56 WI DBB 65,000,000 64,750,000 67,812,6310 57 WI DBB 19,000,000 18,367,060 19,563,520 58 WI DBB 93,562,095 104,640,320 109,175,276 59 WI DBB 147,750,000 155,300,950 189,913,000 90

Serial Number Project Delivery Estimated Design and Contract Design and Final Design and and Location Method Construction Cost Construction Cost Construction Cost 60 WI DBB 37,250,000 37,655,950 38,924,144 61 CA DBB 14,873,000 14,702,560 18,203,424 62 WI DBB 33,350,000 33,990,975 36,711,294 63 CA DBB 24,015,857 18,924,858 21,018,110 64 CA DBB 22,376,600 18,696,902 20,151,138 65 CA DBB 10,085,391 7,725,804 8,234,251 66 WI DBB 3,000,000 2,975,000 3,152,800 67 CA DBB 10,330,708 8,515,310 9,446,070 68 WI DBB 29,500,000 29,986,950 32,400,000 69 WI DBB 540,000 531,800 561,750 70 NV DBB 1,045,000 1,427,964 1,544,453 71 NV DBB 5,700,000 5,400,000 6,500,000 72 NV DBB 550,000 424,255 469,520 73 CA DBB 12,726,000 10,795,557 11,848,259 74 CA DBB 2,875,000 2,451,937 2,588,236 75 CA DBB 20,961,000 21,014,834 22,520,951 76 MI DBB 9,413,700 9,438,700 10,471,045 77 MI DBB 11,070,309 11,045,500 12,185,565 78 FL DBB 5,145,000 5,191,000 5,191,000 79 CA DBB 20,041,855 18,563,155 20,132,981 80 NV DBB 8,200,000 6,900,000 9,344,000 81 NV DBB 340,000 338,388 360,564 82 NV DBB 16,500,000 15,356,677 16,392,677 83 NV DBB 55,000 67,200 68,536 84 CA DBB 2,095,000 2,410,072 2,856,960 Serial Number Project Delivery Contract Award Cost Construction Cost Total Cost and Location Method Growth Growth Growth 01 CA DB -7.32% 1.71% -5.74 02 AZ DB -7.73% 0.00% -7.73 03 AZ DB -2.58% 0.00% -2.58 04 AZ DB -3.79% -2.35% -6.06 05 ND DB -0.83% 0.00% -0.83 06 OK DB -0.67% 0.00% -0.67 07 OK DB -9.64% 0.00% -9.64 08 OK DB 21.07% 32.84% 60.82 09 NV DB -16.07% 20.53% 1.17 10 CA DB -12.08% 40.33% 23.38 11 CA DB 0.00% 44.75% 44.75 12 CA DB -0.81% 32.48% 31.40 13 CA DB -0.17% 25.59% 25.37 14 FL DB -2.95% 17.28% 13.83 15 CA DB -33.33% 50.00% 0.00 16 MI DB 9.38% 8.66% 18.85 17 CA DB -24.90% 29.85% -2.48 18 CA DB -15.86% 14.04% -4.05 19 CA DB -7.05% 6.71% -0.81 20 CA DB -20.07% 19.94% -4.14 21 CA DB -6.77% 0.79% -6.04 22CA DB -26.56% 0.00% -26.56 91

Serial Number Project Delivery Contract Award Cost Construction Cost Total Cost and Location Method Growth Growth Growth 23 CA DB -24.57% 13.41% -14.45 24 CA DB -16.80% 3.35% -14.01 25 MI DB -20.67% 33.33% 5.77 26 AZ DB -0.81% 4.65% 3.80 27 AZ DB -9.17% 41.17% 28.22 28 WY DB 16.86% 3.68% 21.16 29 WY DB 1.19% 2.61% 3.83 30 CO DB -14.98% 10.41% -6.13 31 AZ DB -17.60% 0.00% -17.60 32 AZ DB -19.41% 21.15% -2.36 33 AZ DB -28.63% 29.34% -7.69 34 AZ DB -1.51% 0.00% -1.51 35 AZ DB -21.43% 19.62% -6.02 36 ND DB -4.23% 0.00% -4.23 37 DB -12.35% 16.21% 1.86 38 CA DB -18.78% 21.47% -1.33 39 CA DB -29.35% 46.81% 3.73 40 CA DB -34.89% 51.30% -1.49 41 CA DB -20.60% 27.70% 1.40 42 CA DB -20.83% 18.33% -6.32 43 WI DBB -21.50% 19.47% -6.21 44 WI DBB -4.02% 4.93% 0.71 45 WI DBB 4.19% 4.29% 8.67 46 AZ DBB -1.92% 34.18% 31.60 47 AZ DBB 21.66% 24.41% 51.35 48 AZ DBB 4.60% 7.04% 11.96 49 AZ DBB 0.63% 5.60% 6.27 50 WY DBB 9.25% 11.87% 22.22 51 NV DBB -26.08% 40.90% 4.15 52 NV DBB -13.55% 17.72% 1.77 53 CA DBB -5.88% 9.45% 3.02 54 NV DBB -21.84% 6.26% -16.95 55 WI DBB 13.32% 5.74% 19.83 56 WI DBB -0.38% 4.73% 4.33 57 WI DBB -3.33% 6.51% 2.97 58 WI DBB 11.84% 4.33% 16.69 59 WI DBB 5.11% 22.29% 28.54 60 WI DBB 1.09% 3.37% 4.49 61 CA DBB -1.15% 23.81% 22.39 62 WI DBB 1.92% 8.00% 10.08 63 CA DBB -21.20% 11.06% -12.48 64 CA DBB -16.44% 7.78% -9.95 65 CA DBB -23.40% 6.58% -18.35 66 WI DBB -0.83% 5.98% 5.09 67 CA DBB -17.57% 10.93% -8.56 68 WI DBB 1.65% 8.05% 9.83 69 WI DBB -1.52% 5.63% 4.03 70 NV DBB 36.65% 8.16% 47.79 71 NV DBB -5.26% 20.37% 14.04 72 NV DBB -22.86% 10.67% -14.63 92

Serial Number Project Delivery Contract Award Cost Construction Cost Total Cost and Location Method Growth Growth Growth 73 CA DBB -15.17% 9.75% -6.90 74 CA DBB -14.72% 5.56% -9.97 75 CA DBB 0.26% 7.17% 7.44 76 MI DBB 0.27% 10.94% 11.23 77 MI DBB -0.22% 10.32% 10.07 78 FL DBB 0.89% 0.00% 0.89 79 CA DBB -7.38% 8.46% 0.45 80 NV DBB -15.85% 35.42% 13.95 81 NV DBB -0.47% 6.55% 6.05 82 NV DBB -6.93% 6.75% -0.65 83 NV DBB 22.18% 1.99% 24.61 84 CA DBB 15.04% 18.54% 36.37

APPENDIX D

Schedule Data for DB and DBB Projects

Serial Project Contract Procurement Estimated Design Month of Number and Delivery Duration Duration Notice to Location Method (months) (months) Proceed 01 CA DB 2 10 Jun-07 02 AZ DB 2 10 Sep-02 03 AZ DB 2 12 Nov-05 04 AZ DB 1 6 May-05 05 ND DB 2 12 Nov-07 06 OK DB 2 3 Sep-04 07 OK DB 2 3 Aug-04 08 OK DB 2 1 Nov-09 09 NV DB 4 9 Aug-05 10 CA DB 4 12 Aug-98 11 CA DB 7 4 Nov-06 12 CA DB 5 5 Dec-06 13 CA DB 4 4 Jul-07 14 FL DB 3 10 Feb-06 15 CA DB 3 24 Jan-07 16 MI DB 10 10 May-05 17 CA DB 2 10 Apr-04 18 CA DB 6 6 May-06 19 CA DB 3 12 Jul-05 20 CA DB 5 11 Mar-04 21 CA DB 3 12 Sep-06 22CA DB 3 6 May-07 23 CA DB 2 12 Mar-00 24 CA DB 4 8 Jun-06 25 MI DB 1 10 Feb-02 26 AZ DB 1 24 Aug-99 27 AZ DB 6 5 Jan-04 28 WY DB 1 don’t have Jun-06 29 WY DB 1 don’t have Aug-05 30 CO DB 1 don’t have Apr-08 31 AZ DB 2 7 Dec-06 32 AZ DB 3 8 Apr-02 33 AZ DB 2 6 Nov-04 34 AZ DB 2 4 May-03 35 AZ DB 2 3 Jan-07 36 ND DB 3 5 Sep-02 37 DB 5 6 January-08 38 CA DB 3 6 May-06 39 CA DB 2 6 May-04 40 CA DB 1 8 January-07 41 CA DB 8 8 January-07

Serial Project Contract Procurement Estimated Design Month of Number and Delivery Duration Duration Notice to Location Method (months) (months) Proceed 42 CA DB 2 10 June-04 43 WI DBB 4 8 August-08 44 WI DBB 4 4 May-06 45 WI DBB 2 16 March-04 46 AZ DBB 6 6 June-08 47 AZ DBB 5 5 February-00 48 AZ DBB 6 9 March-02 49 AZ DBB 3 3 August-00 50 WY DBB 1 12 July-07 51 NV DBB 1 6 March-07 52 NV DBB 4 15 May-96 53 CA DBB 2 10 July-03 54 NV DBB 3 3 September-08 55 WI DBB 3 10 June-04 56 WI DBB 4 8 February-04 57 WI DBB 2 36 January-01 58 WI DBB 7 25 January-02 59 WI DBB 4 24 July-06 60 WI DBB 2 10 July-07 61 CA DBB 2 10 May-03 62 WI DBB 4 24 June-06 63 CA DBB 2 20 May-01 64 CA DBB 1 15 March-02 65 CA DBB 2 20 March-00 66 WI DBB 5 9 August-05 67 CA DBB 4 6 November-03 68 WI DBB 15 6 October-03 69 WI DBB 3 8 May-05 70 NV DBB 5 11 October-01 71 NV DBB 2 5 February-02 72 NV DBB 6 4 March-03 73 CA DBB 3 16 January-03 74 CA DBB 10 10 October-02 75 CA DBB 2 11 May-02 76 MI DBB 10 16 June-06 77 MI DBB 1 6 October-07 78 FL DBB 6 8 March-07 79 CA DBB 3 12 June-05 80 NV DBB 2 4 May-03 81 NV DBB 5 2 November-05 82 NV DBB 2 12 May-06 83 NV DBB 1 4 February-07 84 CA DBB 2 13 May-01

Serial Number Project Delivery Final Design Duration Estimated NTP and Location Method (months) Construction Construction Duration Duration (months) (months) 01 CA DB 9 32 42 02 AZ DB 8 13 13 03 AZ DB 12 18 18 04 AZ DB 6 14 14 05 ND DB 15 18 18 06 OK DB 4 10 16 07 OK DB 3 11 11 08 OK DB 2 2 2 09 NV DB 12 18 18 10 CA DB 24 24 24 11 CA DB 2 24 24 12 CA DB 3 26 26 13 CA DB 2 21 21 14 FL DB 9 13 13 15 CA DB 21 24 24 16 MI DB 10 12 12 17 CA DB 10 12 11 18 CA DB 6 24 24 19 CA DB 10 28 32 20 CA DB 9 34 30 21 CA DB 9 30 28 22CA DB 8 20 20 23 CA DB 14 31 31 24 CA DB 9 31 30 25 MI DB 7 7 7 26 AZ DB 27 36 36 27 AZ DB 5 12 12 28 WY DB 6 don’t have don’t have 29 WY DB 4 don’t have don’t have 30 CO DB 11 15 15 31 AZ DB 5 21 21 32 AZ DB 7 16 16 33 AZ DB 6 13 13 34 AZ DB 4 8 8 35 AZ DB 3 9 9 36 ND DB 6 10 15 37 DB 4 13 13 38 CA DB 7 32 32 39 CA DB 6 24 24 40 CA DB 6 20 15 41 CA DB 7 15 15 42 CA DB 11 30 30 43 WI DBB 7 12 12 44 WI DBB 4 9 9 45 WI DBB 17 18 18 46 AZ DBB 9 11 11

Serial Number Project Delivery Final Design Duration Estimated NTP and Location Method (months) Construction Construction Duration Duration (months) (months) 47 AZ DBB 9 7 7 48 AZ DBB 11 10 10 49 AZ DBB 11 12 12 50 WY DBB 12 7 7 51 NV DBB 6 12 12 52 NV DBB 28 20 20 53 CA DBB 13 28 28 54 NV DBB 3 3 3 55 WI DBB 13 18 18 56 WI DBB 8 18 18 57 WI DBB 44 24 24 58 WI DBB 30 28 28 59 WI DBB 25 20 20 60 WI DBB 12 19 19 61 CA DBB 11 20 20 62 WI DBB 27 18 18 63 CA DBB 22 30 30 64 CA DBB 15 26 26 65 CA DBB 23 18 18 66 WI DBB 12 6 6 67 CA DBB 8 16 16 68 WI DBB 18 20 20 69 WI DBB 12 4 4 70 NV DBB 14 16 16 71 NV DBB 5 11 11 72 NV DBB 10 3 3 73 CA DBB 15 30 30 74 CA DBB 12 15 15 75 CA DBB 12 28 28 76 MI DBB 16 12 12 77 MI DBB 6 13 13 78 FL DBB 15 15 15 79 CA DBB 12 24 24 80 NV DBB 4 5 5 81 NV DBB 22 4 4 82 NV DBB 11 12 12 83 NV DBB 5 3 3 84 CA DBB 17 28 28 Serial Number Project Delivery Final Construction Estimated Design NTP Design and and Location Method Duration and Construction Construction (months) Duration Duration (months) (months) 01 CA DB 32 42 42 02 AZ DB 12 18 18 03 AZ DB 16 24 24 04 AZ DB 12 20 20 05 ND DB 20 30 30

Serial Number Project Delivery Final Construction Estimated Design NTP Design and and Location Method Duration and Construction Construction (months) Duration Duration (months) (months) 06 OK DB 14 16 16 07 OK DB 10 14 14 08 OK DB 4 3 3 09 NV DB 17 18 18 10 CA DB 27 30 30 11 CA DB 25 28 28 12 CA DB 28 31 31 13 CA DB 23 25 25 14 FL DB 18 23 23 15 CA DB 26 36 60 16 MI DB 15 22 22 17 CA DB 10 36 36 18 CA DB 23 25 25 19 CA DB 25 41 41 20 CA DB 24 45 45 21 CA DB 23 42 42 22CA DB 17 23 23 23 CA DB 28 38 38 24 CA DB 28 40 40 25 MI DB 10 13 13 26 AZ DB 41 36 36 27 AZ DB 13 26 26 28 WY DB 12 12 12 29 WY DB 7 8 8 30 CO DB 14 24 24 31 AZ DB 18 28 28 32 AZ DB 13 24 24 33 AZ DB 10 19 19 34 AZ DB 5 12 12 35 AZ DB 8 12 12 36 ND DB 13 15 15 37 DB 13 20 20 38 CA DB 31 38 38 39 CA DB 24 28 28 40 CA DB 15 20 20 41 CA DB 12 22 22 42 CA DB 26 28 28 43 WI DBB 11 20 19 44 WI DBB 11 13 13 45 WI DBB 21 34 35 46 AZ DBB 15 17 20 47 AZ DBB 11 12 16 48 AZ DBB 12 19 21 49 AZ DBB 12 15 23 50 WY DBB 8 19 19 51 NV DBB 12 18 18 52 NV DBB 29 35 48 53 CA DBB 30 38 41 98

Serial Number Project Delivery Final Construction Estimated Design NTP Design and and Location Method Duration and Construction Construction (months) Duration Duration (months) (months) 54 NV DBB 3 6 6 55 WI DBB 20 28 31 56 WI DBB 18 26 26 57 WI DBB 26 60 68 58 WI DBB 32 53 58 59 WI DBB 24 44 45 60 WI DBB 21 29 31 61 CA DBB 24 30 31 62 WI DBB 20 42 45 63 CA DBB 29 50 52 64 CA DBB 28 41 41 65 CA DBB 17 38 41 66 WI DBB 8 15 18 67 CA DBB 17 22 24 68 WI DBB 24 26 38 69 WI DBB 5 12 16 70 NV DBB 23 27 30 71 NV DBB 11 16 16 72 NV DBB 6 7 13 73 CA DBB 29 46 45 74 CA DBB 17 25 27 75 CA DBB 30 39 40 76 MI DBB 12 28 28 77 MI DBB 13 19 19 78 FL DBB 21 23 30 79 CA DBB 26 36 36 80 NV DBB 5 9 9 81 NV DBB 5 6 26 82 NV DBB 18 24 23 83 NV DBB 3 7 8 84 CA DBB 33 41 45

Serial Number Project Final Design Design and Total Schedule Schedule and Location Delivery and Construction Growth Intensity Method Construction Schedule (Months) (SF/Day) Duration Growth (months) (months) 01 CA DB 38 -0.0952381 -9.52381 173.44 02 AZ DB 16 -0.1111111 -11.11111 298.30 03 AZ DB 20 -0.1666667 -16.66667 196.64 04 AZ DB 14 -0.3000000 -30.00000 588.91 05 ND DB 25 -0.1666667 -16.66667 136.36 06 OK DB 14 -0.1250000 -12.50000 68.18 99

Serial Number Project Final Design Design and Total Schedule Schedule and Location Delivery and Construction Growth Intensity Method Construction Schedule (Months) (SF/Day) Duration Growth (months) (months) 07 OK DB 12 -0.1428571 -14.28571 30.30 08 OK DB 5 0.6666667 66.66667 12.27 09 NV DB 17 -0.0555556 -5.55556 117.65 10 CA DB 27 -0.1000000 -10.00000 336.70 11 CA DB 27 -0.0357143 -3.57143 195.53 12 CA DB 31 0.0000000 0.00000 179.57 13 CA DB 25 0.0000000 0.00000 136.22 14 FL DB 26 0.1304348 13.04348 76.92 15 CA DB 60 0.0000000 66.66667 39.39 16 MI DB 26 0.1818182 18.18182 203.56 17 CA DB 35 -0.0277778 -2.77778 145.22 18 CA DB 26 0.0400000 4.00000 93.88 19 CA DB 40 -0.0243902 -2.43902 101.65 20 CA DB 41 -0.0888889 -8.88889 116.00 21 CA DB 37 -0.1190476 -11.90476 161.03 22CA DB 22 -0.0434783 -4.34783 21.88 23 CA DB 33 -0.1315789 -13.15789 89.14 24 CA DB 32 -0.2000000 -20.00000 70.98 25 MI DB 16 0.2307692 23.07692 51.14 26 AZ DB 41 0.1388889 13.88889 449.00 27 AZ DB 26 0.0000000 0.00000 156.16 28 WY DB 12 0.0000000 0.00000 303.03 29 WY DB 9 0.1250000 12.50000 40.10 30 CO DB 23 -0.0416667 -4.16667 116.60 31 AZ DB 23 -0.1785714 -17.85714 474.31 32 AZ DB 20 -0.1666667 -16.66667 195.45 33 AZ DB 14 -0.2631579 -26.31579 157.18 34 AZ DB 8 -0.3333333 -33.33333 2218.39 35 AZ DB 11 -0.0833333 -8.33333 175.80 36 ND DB 13 -0.1333333 -13.33333 80.42 37 DB 17 -0.1500000 -15.00000 58.82 38 CA DB 34 -0.1052632 -10.52632 118.04 39 CA DB 27 -0.0357143 -3.57143 144.44 40 CA DB 18 -0.1000000 -10.00000 61.87 41 CA DB 19 -0.1363636 -13.63636 30.38 42 CA DB 26 -0.0714286 -7.14286 107.96 43 WI DBB 18 -0.0526316 -10.00000 160.61 44 WI DBB 15 0.1538462 15.38462 93.94 45 WI DBB 38 0.0857143 11.76471 162.42 46 AZ DBB 24 0.2000000 41.17647 9.85 47 AZ DBB 20 0.2500000 66.66667 37.30 48 AZ DBB 23 0.0952381 21.05263 60.36 49 AZ DBB 23 0.0000000 53.33333 100.42 50 WY DBB 20 0.0526316 5.26316 7.68 51 NV DBB 18 0.0000000 0.00000 20.96 52 NV DBB 57 0.1875000 62.85714 19.38 53 CA DBB 43 0.0487805 13.15789 116.28 100

Serial Number Project Final Design Design and Total Schedule Schedule and Location Delivery and Construction Growth Intensity Method Construction Schedule (Months) (SF/Day) Duration Growth (months) (months) 54 NV DBB 6 0.0000000 0.00000 5.91 55 WI DBB 33 0.0645161 17.85714 215.95 56 WI DBB 26 0.0000000 0.00000 172.69 57 WI DBB 70 0.0294118 16.66667 31.49 58 WI DBB 62 0.0689655 16.98113 142.46 59 WI DBB 49 0.0888889 11.36364 166.98 60 WI DBB 33 0.0645161 13.79310 110.19 61 CA DBB 35 0.1290323 16.66667 59.21 62 WI DBB 47 0.0444444 11.90476 58.03 63 CA DBB 51 -0.0192308 2.00000 141.35 64 CA DBB 43 0.0487805 4.87805 104.12 65 CA DBB 40 -0.0243902 5.26316 79.55 66 WI DBB 20 0.1111111 33.33333 19.59 67 CA DBB 25 0.0416667 13.63636 81.82 68 WI DBB 42 0.1052632 61.53846 104.00 69 WI DBB 17 0.0625000 41.66667 10.70 70 NV DBB 37 0.2333333 37.03704 8.65 71 NV DBB 16 0.0000000 0.00000 102.27 72 NV DBB 16 0.2307692 128.57143 7.39 73 CA DBB 44 -0.0222222 -4.34783 71.23 74 CA DBB 29 0.0740741 16.00000 33.82 75 CA DBB 42 0.0500000 7.69231 50.05 76 MI DBB 28 0.0000000 0.00000 104.39 77 MI DBB 19 0.0000000 0.00000 175.11 78 FL DBB 36 0.2000000 56.52174 27.78 79 CA DBB 38 0.0555556 5.55556 50.96 80 NV DBB 9 0.0000000 0.00000 181.82 81 NV DBB 27 0.0384615 350.00000 1.20 82 NV DBB 29 0.2608696 20.83333 25.08 83 NV DBB 8 0.0000000 14.28571 1.79 84 CA DBB 50 0.1111111 21.95122 7.27

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APPENDIX E

Change-Order Data for DB and DBB Projects

Serial Number Project Delivery Number of Cost of Design Number of and Location Method Design Change Change Orders Construction Orders Change Orders 01 CA DB 5 96,452 8 02 AZ DB 0 0 0 03 AZ DB 0 0 0 04 AZ DB 1 (373,350) 0 05 ND DB 0 0 0 06 OK DB 16 0 17 07 OK DB 5 0 11 08 OK DB 0 0 0 09 NV DB 8 389,000 0 10 CA DB 15 200,000 91 11 CA DB 7 3,718,656 7 12 CA DB 18 567,210 24 13 CA DB 6 128,952 25 14 FL DB 0 0 0 15 CA DB 0 0 0 16 MI DB 0 0 43 17 CA DB 20 0 19 18 CA DB 65 242,630 42 19 CA DB 45 145,904 92 20 CA DB 60 2,514,620 102 21 CA DB 6 (1,571,166) 0 22CA DB 0 0 0 23 CA DB 31 652,123 92 24 CA DB 90 409,193 105 25 MI DB 39 0 39 26 AZ DB 0 0 20 27 AZ DB 1 4,000 6 28 WY DB 6 365,955 0 29 WY DB 3 33,008 0 30 CO DB unknown 0 unknown 31 AZ DB 0 0 0 32 AZ DB 5 1,800,000 0 33 AZ DB 2 822,000 0 34 AZ DB 0 0 0 35 AZ DB 6 726,850 2 36 ND DB 0 0 0 37 DB 1 800 45 38 CA DB 18 974,840 26 39 CA DB 18 632,519 10 40 CA DB 23 191,676 24 41 CA DB 0 0 0 42 CA DB 6 1,314,923 0 102

Serial Number Project Delivery Number of Cost of Design Number of and Location Method Design Change Change Orders Construction Orders Change Orders 43 WI DBB 6 59,872 14 44 WI DBB 5 64,521 33 45 WI DBB 8 239,838 41 46 AZ DBB 9 129,693 35 47 AZ DBB 3 96,345 17 48 AZ DBB 4 88,621 22 49 AZ DBB 5 152,966 2 50 WY DBB 0 0 4 51 NV DBB 20 101,000 20 52 NV DBB 9 82,338 20 53 CA DBB 155 2,151,312 30 54 NV DBB 0 0 2 55 WI DBB 23 401,783 42 56 WI DBB 16 823,641 71 57 WI DBB 13 321,578 22 58 WI DBB 41 1,584,267 73 59 WI DBB 59 3,542,879 122 60 WI DBB 13 168,492 29 61 CA DBB 86 538,121 240 62 WI DBB 11 211,384 86 63 CA DBB 134 487,695 79 64 CA DBB 84 667,078 111 65 CA DBB 32 172,686 26 66 WI DBB 6 67,522 11 67 CA DBB 87 253,687 76 68 WI DBB 9 961,567 36 69 WI DBB 3 5,286 9 70 NV DBB 5 62,323 12 71 NV DBB 4 300,000 4 72 NV DBB 6 13,151 13 73 CA DBB 8 17,811 117 74 CA DBB 25 58,683 22 75 CA DBB 59 465,491 128 76 MI DBB 84 535,971 103 77 MI DBB 0 0 106 78 FL DBB not available not available not available 79 CA DBB 66 550,317 120 80 NV DBB 5 522,000 5 81 NV DBB 1 3,520 4 82 NV DBB 8 518,000 8 83 NV DBB 0 0 2 84 CA DBB 7 11,576 40 Serial Number Project Delivery Cost of Total Number of Total Cost of and Location Method Construction Change Orders Design and Change Orders Construction Change Orders 01 CA DB 536,121 13 632,573

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Serial Number Project Delivery Cost of Total Number of Total Cost of and Location Method Construction Change Orders Design and Change Orders Construction Change Orders 02 AZ DB - 0 - 03 AZ DB - 0 - 04 AZ DB - 1 (373,350) 05 ND DB - 0 - 06 OK DB - 33 - 07 OK DB - 16 - 08 OK DB - 0 - 09 NV DB - 8 389,000 10 CA DB 2,340,032 106 2,540,032 11 CA DB 3,718,655 14 7,437,311 12 CA DB 1,400,297 42 1,967,507 13 CA DB 856,274 31 985,226 14 FL DB - 0 - 15 CA DB - 0 - 16 MI DB 562,000 43 562,000 17 CA DB - 39 - 18 CA DB 69,880 107 312,510 19 CA DB 397,571 137 543,475 20 CA DB 1,300,087 162 3,814,707 21 CA DB - 6 (1,571,166) 22CA DB - 0 - 23 CA DB 1,139,939 123 1,792,062 24 CA DB 667,078 195 1,076,271 25 MI DB 340,028 78 340,028 26 AZ DB 206,902 20 206,902 104

Serial Number Project Delivery Cost of Total Number of Total Cost of and Location Method Construction Change Orders Design and Change Orders Construction Change Orders 27 AZ DB 726,498 7 730,498 28 WY DB - 6 365,955 29 WY DB - 3 33,008 30 CO DB 173,250 0 173,250 31 AZ DB - 0 - 32 AZ DB - 5 1,800,000 33 AZ DB - 2 822,000 34 AZ DB - 0 - 35 AZ DB 338,150 8 1,065,000 36 ND DB - 0 - 37 DB 259,060 46 259,860 38 CA DB 700,500 44 1,675,341 39 CA DB 187,410 28 819,929 40 CA DB 731,179 47 922,855 41 CA DB - 0 - 42 CA DB - 6 1,314,923 43 WI DBB 828,226 20 888,098 44 WI DBB 288,280 38 352,801 45 WI DBB 920,662 49 1,160,500 46 AZ DBB 579,710 44 709,403 47 AZ DBB 414,005 20 510,350 48 AZ DBB 202,879 26 291,500 49 AZ DBB 125,151 7 278,117 50 WY DBB 120,365 4 120,365 105

Serial Number Project Delivery Cost of Total Number of Total Cost of and Location Method Construction Change Orders Design and Change Orders Construction Change Orders 51 NV DBB 101,000 40 202,000 52 NV DBB 248,634 29 330,972 53 CA DBB 803,365 185 2,954,677 54 NV DBB 5,400 2 5,400 55 WI DBB 1,104,217 65 1,506,000 56 WI DBB 2,138,990 87 2,962,631 57 WI DBB 740,422 35 1,062,000 58 WI DBB 2,808,689 114 4,392,956 59 WI DBB 30,519,171 181 34,062,050 60 WI DBB 948,289 42 1,116,781 61 CA DBB 2,520,401 326 3,058,522 62 WI DBB 2,423,935 97 2,635,319 63 CA DBB 1,253,716 213 1,741,411 64 CA DBB 409,013 195 1,076,091 65 CA DBB 177,394 58 350,080 66 WI DBB 93,478 17 161,000 67 CA DBB 570,913 163 824,600 68 WI DBB 1,338,433 45 2,300,000 69 WI DBB 24,664 12 29,950 70 NV DBB 25,667 17 87,990 71 NV DBB 300,000 8 600,000 72 NV DBB 28,614 19 41,765 73 CA DBB 823,994 125 841,804 74 CA DBB 27,399 47 86,083 75 CA DBB 632,769 187 1,098,260 106

Serial Number Project Delivery Cost of Total Number of Total Cost of and Location Method Construction Change Orders Design and Change Orders Construction Change Orders 76 MI DBB 408,886 187 944,857 77 MI DBB 1,140,065 106 1,140,065 78 FL DBB not available 0 - 79 CA DBB 580,914 186 1,131,231 80 NV DBB 522,000 10 1,044,000 81 NV DBB 16,136 5 19,656 82 NV DBB 518,000 16 1,036,000 83 NV DBB 1,336 2 1,336 84 CA DBB 391,313 47 402,889

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APPENDIX F

DESIGN-BUILD INSTITUTE OF AMERICA

STATE PUBLIC PROCUREMENT LAWS

Number of states where public agencies are permitted to use DB 20 states use DB for all types of design and construction projects 18 states DB is widely permitted but not all agencies are permitted to use DB 12 states DB is a limited option.

DBIA (2011) ”Design-Build State Procurement Map”

VITA

Graduate College University of Nevada, Las Vegas

James David Fernane

Degrees: Bachelor of Architecture University of California, Berkeley

Thesis Title: Comparison of Design-Build and Design-Bid-Build Performance of Public University Projects.

Thesis Examination Committee: Chairperson, Pramen P. Shrestha, Ph.D.,P.E. Committee Member, David Shields, Ph.D., P.E. Committee Member, Neil D. Opfer, CPC, CCE, PMP Graduate Faculty Representative, Nancy N. Menzel, Ph.D .

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